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  • 3051.
    Zhu, Xiaomeng
    et al.
    Scania CV AB (publ), Södertälje, Sweden ; KTH Royal Institute of Technology, Stockholm, Sweden.
    Björkman, Mårten
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Maki, Atsuto
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Hanson, Lars
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Mårtensson, Pär
    Scania CV AB (publ), Södertälje, Sweden.
    Surface Defect Detection with Limited Training Data: A Case Study on Crown Wheel Surface Inspection2023In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 120, p. 1333-1338Article in journal (Refereed)
    Abstract [en]

    This paper presents an approach to automatic surface defect detection by a deep learning-based object detection method, particularly in challenging scenarios where defects are rare, i.e., with limited training data. We base our approach on an object detection model YOLOv8, preceded by a few steps: 1) filtering out irrelevant information, 2) enhancing the visibility of defects, namely brightness contrast, and 3) increasing the diversity of the training data through data augmentation. We evaluated the method in an industrial case study of crown wheel surface inspection in detecting Unclean Gear as well as Deburring defects, resulting in promising performances. With the combination of the three preprocessing steps, we improved the detection accuracy by 22.2% and 37.5% respectively while detecting those two defects. We believe that the proposed approach is also adaptable to various applications of surface defect detection in other industrial environments as the employed techniques, such as image segmentation, are available off the shelf. 

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  • 3052.
    Zia, Hamza
    et al.
    Chung Ang University, South Korea.
    Soomro, Shafiullah
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Choi, Kwang Nam
    Chung Ang University, South Korea.
    Image Segmentation Using Bias Correction Active Contours2024In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 60641-60655Article in journal (Refereed)
    Abstract [en]

    Deep learning-based image segmentation methods require densely annotated and massive datasets to produce effective results. On the other hand, active contours-based methods are excellent alternatives to the situation, producing acceptable segmentation results. Earlier active contour models, including local and global region information, struggle with their limitations, such as spurious contours appearing in inhomogeneous images. Bias correction is utilized to solve the bias field's energy, considering the intensity inhomogeneity and the level set functions that suggest an image domain division. In our approach, we combine the advantages of local and global information in the image level set function, resulting in a combined energy function that aids in the efficient evolution of contours on images and can judge the relevance of the item and its surroundings. The proposed model computes data force by extracting local information from an in-homogeneous image using image-fitting energy and then computing all pixel values simultaneously. Objects with high differences between grey levels or more in-homogeneity can be segmented. The outcome demonstrates that our method is more dependable and computationally efficient than previous methods.

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  • 3053.
    Ziegler, Thomas
    et al.
    ETH Eidgenössische Technische, Hochschule, Zürich.
    Butepage, Judith
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Welle, Michael C.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Varava, Anastasiia
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Novkovic, Tonci
    ETH Eidgenössische Technische, Hochschule, Zürich.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Fashion Landmark Detection and Category Classification for Robotics2020In: Proceedings IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC 2020), 2020Conference paper (Refereed)
    Abstract [en]

    Research on automated, image based identification of clothing categories and fashion landmarks has recently gained significant interest due to its potential impact on areas such as robotic clothing manipulation, automated clothes sorting and recycling, and online shopping. Several public and annotated fashion datasets have been created to facilitate research advances in this direction. In this work, we make the first step towards leveraging the data and techniques developed for fashion image analysis in vision-based robotic clothing manipulation tasks. We focus on techniques that can generalize from large-scale fashion datasets to less structured, small datasets collected in a robotic lab. Specifically, we propose training data augmentation methods such as elastic warping, and model adjustments such as rotation invariant convolutions to make the model generalize better. Our experiments demonstrate that our approach outperforms stateof-the art models with respect to clothing category classification and fashion landmark detection when tested on previously unseen datasets. Furthermore, we present experimental results on a new dataset of images where a robot holds different garments, collected in our lab.

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  • 3054.
    Zins, Matthieu
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Color Fusion and Super-Resolution for Time-of-Flight Cameras2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The recent emergence of time-of-flight cameras has opened up new possibilities in the world of computer vision. These compact sensors, capable of recording the depth of a scene in real-time, are very advantageous in many applications, such as scene or object reconstruction. This thesis first addresses the problem of fusing depth data with color images. A complete process to combine a time-of-flight camera with a color camera is described and its accuracy is evaluated. The results show that a satisfying precision is reached and that the step of calibration is very important.

    The second part of the work consists of applying super-resolution techniques to the time-of-flight camera in order to improve its low resolution. Different types of super-resolution algorithms exist but this thesis focuses on the combination of multiple shifted depth maps. The proposed framework is made of two steps: registration and reconstruction. Different methods for each step are tested and compared according to the improvements reached in term of level of details, sharpness and noise reduction. The results obtained show that Lucas-Kanade performs the best for the registration and that a non-uniform interpolation gives the best results in term of reconstruction. Finally, a few suggestions are made about future work and extensions for our solutions.

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  • 3055.
    Zitinski Elias, Paula
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Nyström, Daniel
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Gooran, Sasan
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Color separation for improved perceived image quality in terms of graininess and gamut2017In: Color Research and Application, ISSN 0361-2317, E-ISSN 1520-6378, Vol. 42, no 4, p. 486-497Article in journal (Refereed)
    Abstract [en]

    Multi-channel printing employs additional inks to improve the perceived image quality by reducing the graininess and augmenting the printer gamut. It also requires a color separation that deals with the one-to-many mapping problem imposed when using more than three inks. The proposed separation model incorporates a multilevel halftoning algorithm, reducing the complexity of the print characterization by grouping inks of similar hues in the same channel. In addition, a cost function is proposed that weights selected factors influencing the print and perceived image quality, namely color accuracy, graininess and ink consumption. The graininess perception is qualitatively assessed using S-CIELAB, a spatial low-pass filtering mimicking the human visual system. By applying it to a large set of samples, a generalized prediction quantifying the perceived graininess is carried out and incorporated as a criterion in the color separation. The results of the proposed model are compared with the separation giving the best colorimetric match, showing improvements in the perceived image quality in terms of graininess at a small cost of color accuracy and ink consumption. (c) 2016 Wiley Periodicals, Inc.

  • 3056.
    Zobel, Valentin
    et al.
    Zuse Institue Berlin.
    Reininghaus, Jan
    Zuse Institue Berlin.
    Hotz, Ingrid
    Zuse Institue Berlin.
    Visualization of Two-Dimensional Symmetric Tensor Fields Using the Heat Kernel Signature2014In: Topological Methods in Data Analysis and Visualization: Theory, Algorithms, and Applications / [ed] Peer-Timo Bremer, Ingrid Hotz, Valerio Pascucci, Ronald Peikert, Springer, 2014, p. 249-262Chapter in book (Refereed)
    Abstract [en]

    We propose a method for visualizing two-dimensional symmetric positive definite tensor fields using the Heat Kernel Signature (HKS). The HKS is derived from the heat kernel and was originally introduced as an isometry invariant shape signature. Each positive definite tensor field defines a Riemannian manifold by considering the tensor field as a Riemannian metric. On this Riemmanian manifold we can apply the definition of the HKS. The resulting scalar quantity is used for the visualization of tensor fields. The HKS is closely related to the Gaussian curvature of the Riemannian manifold and the time parameter of the heat kernel allows a multiscale analysis in a natural way. In this way, the HKS represents field related scale space properties, enabling a level of detail analysis of tensor fields. This makes the HKS an interesting new scalar quantity for tensor fields, which differs significantly from usual tensor invariants like the trace or the determinant. A method for visualization and a numerical realization of the HKS for tensor fields is proposed in this chapter. To validate the approach we apply it to some illustrating simple examples as isolated critical points and to a medical diffusion tensor data set.

  • 3057.
    Zobel, Valentin
    et al.
    Leipzig University, Leipzig, Germany.
    Reininghaus, Jan
    Institute of Science and Technology Austria, Klosterneuburg, Austria.
    Hotz, Ingrid
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Visualizing Symmetric Indefinite 2D Tensor Fields using the Heat Kernel Signature2015In: Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data / [ed] Ingrid Hotz, Thomas Schultz, Cham: Springer, 2015, p. 257-267Chapter in book (Refereed)
    Abstract [en]

    The Heat Kernel Signature (HKS) is a scalar quantity which is derived from the heat kernel of a given shape. Due to its robustness, isometry invariance, and multiscale nature, it has been successfully applied in many geometric applications. From a more general point of view, the HKS can be considered as a descriptor of the metric of a Riemannian manifold. Given a symmetric positive definite tensor field we may interpret it as the metric of some Riemannian manifold and thereby apply the HKS to visualize and analyze the given tensor data. In this paper, we propose a generalization of this approach that enables the treatment of indefinite tensor fields, like the stress tensor, by interpreting them as a generator of a positive definite tensor field. To investigate the usefulness of this approach we consider the stress tensor from the two-point-load model example and from a mechanical work piece.

  • 3058.
    Zografos, Vasileios
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Enhancing motion segmentation by combination of complementary affinities2012In: Proceedings of the 21st Internationa Conference on Pattern Recognition, 2012, p. 2198-2201Conference paper (Other academic)
    Abstract [en]

    Complementary information, when combined in the right way, is capable of improving clustering and segmentation problems. In this paper, we show how it is possible to enhance motion segmentation accuracy with a very simple and inexpensive combination of complementary information, which comes from the column and row spaces of the same measurement matrix. We test our approach on the Hopkins155 dataset where it outperforms all other state-of-the-art methods.

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  • 3059.
    Zografos, Vasileios
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Lenz, Reiner
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    The Weibull manifold in low-level image processing: an application to automatic image focusing.2013In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 31, no 5, p. 401-417Article in journal (Refereed)
    Abstract [en]

    In this paper, we introduce a novel framework for low-level image processing and analysis. First, we process images with very simple, difference-based filter functions. Second, we fit the 2-parameter Weibull distribution to the filtered output. This maps each image to the 2D Weibull manifold. Third, we exploit the information geometry of this manifold and solve low-level image processing tasks as minimisation problems on point sets. For a proof-of-concept example, we examine the image autofocusing task. We propose appropriate cost functions together with a simple implicitly-constrained manifold optimisation algorithm and show that our framework compares very favourably against common autofocus methods from literature. In particular, our approach exhibits the best overall performance in terms of combined speed and accuracy

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    Weibull_IMAVIS
  • 3060.
    Zuidberg Dos Martires, Pedro
    et al.
    Declaratieve Talen en Artificiele Intelligentie (DTAI), Department of Computer Science, KU Leuven, Leuven, Belgium.
    Kumar, Nitesh
    Declaratieve Talen en Artificiele Intelligentie (DTAI), Department of Computer Science, KU Leuven, Leuven, Belgium.
    Persson, Andreas
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    De Raedt, Luc
    Örebro University, School of Science and Technology. Declaratieve Talen en Artificiele Intelligentie (DTAI), Department of Computer Science, KU Leuven, Leuven, Belgium.
    Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring2020In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 7, article id 100Article in journal (Refereed)
    Abstract [en]

    Robotic agents should be able to learn from sub-symbolic sensor data and, at the same time, be able to reason about objects and communicate with humans on a symbolic level. This raises the question of how to overcome the gap between symbolic and sub-symbolic artificial intelligence. We propose a semantic world modeling approach based on bottom-up object anchoring using an object-centered representation of the world. Perceptual anchoring processes continuous perceptual sensor data and maintains a correspondence to a symbolic representation. We extend the definitions of anchoring to handle multi-modal probability distributions and we couple the resulting symbol anchoring system to a probabilistic logic reasoner for performing inference. Furthermore, we use statistical relational learning to enable the anchoring framework to learn symbolic knowledge in the form of a set of probabilistic logic rules of the world from noisy and sub-symbolic sensor input. The resulting framework, which combines perceptual anchoring and statistical relational learning, is able to maintain a semantic world model of all the objects that have been perceived over time, while still exploiting the expressiveness of logical rules to reason about the state of objects which are not directly observed through sensory input data. To validate our approach we demonstrate, on the one hand, the ability of our system to perform probabilistic reasoning over multi-modal probability distributions, and on the other hand, the learning of probabilistic logical rules from anchored objects produced by perceptual observations. The learned logical rules are, subsequently, used to assess our proposed probabilistic anchoring procedure. We demonstrate our system in a setting involving object interactions where object occlusions arise and where probabilistic inference is needed to correctly anchor objects.

  • 3061.
    Zukas, Paulius
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Raising Awareness of Computer Vision: How can a single purpose focused CV solution be improved?2018Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The concept of Computer Vision is not new or fresh. On contrary ideas have been shared and worked on for almost 60 years. Many use cases have been found throughout the years and various systems developed, but there is always a place for improvement. An observation was made, that methods used today are generally focused on a single purpose and implement expensive technology, which could be improved. In this report, we are going to go through an extensive research to find out if a professionally sold, expensive software, can be replaced by an off the shelf, low-cost solution entirely designed and developed in-house. To do that we are going to look at the history of Computer Vision, examples of applications, algorithms, and find general scenarios or computer vision problems which can be solved. We are then going take a step further and define solid use cases for each of the scenarios found. Finally, a prototype solution is going to be designed and presented. After analysing the results gathered we are going to reach out to the reader convincing him/her that such application can be developed and work efficiently in various areas saving investments to businesses.

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  • 3062.
    Zunic, Jovisa
    et al.
    Faculty of Technical Sciences, University of Novi Sad.
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Faculty of Technical Sciences, University of Novi Sad .
    A characterization of digital disks by discrete moments1997In: International Conference on Computer Analysis of Images and Patterns / [ed] Sommer G., Daniilidis K., Pauli J., Springer, 1997, p. 582-589Conference paper (Refereed)
  • 3063.
    Zunic, Jovisa
    et al.
    Faculty of Technical Sciences, University of Novi Sad.
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Faculty of Technical Sciences, University of Novi Sad.
    Efficiency of Characterizing Ellipses and Ellipsoids by Discrete Moments2000In: IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 22, no 4, p. 407-414Article in journal (Refereed)
    Abstract [en]

    In this paper, our studies are focused on ellipses and problems related to their representation and reconstruction from the data resulting from their digitization. The main result of the paper is that a finite number of discrete moments, corresponded to digital ellipses, is in one-to-one correspondence with digital ellipses, which enables coding of digital ellipses with an asymptotically optimal amount of memory. In addition, the problem of reconstruction, based on the same parameters, is considered. Since the digitization of real shapes causes an inherent loss of information about the original objects, the precision of the original shape estimation from the corresponding digital data is limited. We derive a sharp upper bound for the errors in reconstruction of the center position and half-axes of the ellipse, in function of the applied picture resolution (i.e., the number of pixels per unit). An extension of these results to the 3D case is also given

  • 3064.
    Zwölfer, Michael
    et al.
    Department of Sport Science, University of Innsbruck, 6020, Innsbruck, Austria. michael.zwoelfer@uibk.ac.at.
    Heinrich, Dieter
    Department of Sport Science, University of Innsbruck, 6020, Innsbruck, Austria.
    Wandt, Bastian
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Rhodin, Helge
    Department of Computer Science, University of British Columbia, Vancouver, V6T 1Z4, Canada.
    Spörri, Jörg
    Department of Orthopaedics, Balgrist University Hospital, University of Zurich, 8006, Zurich, Switzerland.
    Nachbauer, Werner
    Department of Sport Science, University of Innsbruck, 6020, Innsbruck, Austria.
    A graph-based approach can improve keypoint detection of complex poses: a proof-of-concept on injury occurrences in alpine ski racing2023In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1, article id 21465Article in journal (Refereed)
    Abstract [en]

    For most applications, 2D keypoint detection works well and offers a simple and fast tool to analyse human movements. However, there remain many situations where even the best state-of-the-art algorithms reach their limits and fail to detect human keypoints correctly. Such situations may occur especially when individual body parts are occluded, twisted, or when the whole person is flipped. Especially when analysing injuries in alpine ski racing, such twisted and rotated body positions occur frequently. To improve the detection of keypoints for this application, we developed a novel method that refines keypoint estimates by rotating the input videos. We select the best rotation for every frame with a graph-based global solver. Thereby, we improve keypoint detection of an arbitrary pose estimation algorithm, in particular for 'hard' keypoints. In the current proof-of-concept study, we show that our approach outperforms standard keypoint detection results in all categories and in all metrics, in injury-related out-of-balance and fall situations by a large margin as well as previous methods, in performance and robustness. The Injury Ski II dataset was made publicly available, aiming to facilitate the investigation of sports accidents based on computer vision in the future.

  • 3065.
    Ärleryd, Sebastian
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Realtime Virtual 3D Image of Kidney Using Pre-Operative CT Image for Geometry and Realtime US-Image for Tracking2014Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this thesis a method is presented to provide a 3D visualization of the human kidney and surrounding tissue during kidney surgery. The method takes advantage of the high detail of 3D X-Ray Computed Tomography (CT) and the high time resolution of Ultrasonography (US). By extracting the geometry from a single preoperative CT scan and animating the kidney by tracking its position in real time US images, a 3D visualization of the surgical volume can be created. The first part of the project consisted of building an imaging phantom as a simplified model of the human body around the kidney. It consists of three parts: the shell part representing surrounding tissue, the kidney part representing the kidney soft tissue and a kidney stone part embedded in the kidney part. The shell and soft tissue kidney parts was cast with a mixture of the synthetic polymer Polyvinyl Alchohol (PVA) and water. The kidney stone part was cast with epoxy glue. All three parts where designed to look like human tissue in CT and US images. The method is a pipeline of stages that starts with acquiring the CT image as a 3D matrix of intensity values. This matrix is then segmented, resulting in separate polygonal 3D models for the three phantom parts. A scan of the model is then performed using US, producing a sequence of US images. A computer program extracts easily recognizable image feature points from the images in the sequence. Knowing the spatial position and orientation of a new US image in which these features can be found again allows the position of the kidney to be calculated. The presented method is realized as a proof of concept implementation of the pipeline. The implementation displays an interactive visualization where the kidney is positioned according to a user-selected US image scanned for image features. Using the proof of concept implementation as a guide, the accuracy of the proposed method is estimated to be bounded by the acquired image data. For high resolution CT and US images, the accuracy can be in the order of a few millimeters. 

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    Masters Thesis - Sebastian Ärleryd
  • 3066.
    Ågren, Isabella
    Linköping University, Department of Computer and Information Science.
    The major organisation leap from ancient to futuristic: An explorative case study investigating a company in the shift towards smart ferries2022Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Many industries stand in front of a significant shift as we have entered the 4th industrial revolution,and society is becoming digitalised to a higher degree. Autonomous and smart ships may be emergingin the coming future within the maritime industry. Previous research has tried understanding thecompetencies needed to work with the autonomous ship. This case study aims to understand a shoreand ship organisation as their company stands on the verge of smart ships arriving. To understandthis, the a) present work has been explored, b) potential changes to the work tasks because of theautomation and c) how the organisation and crew members can meet the changes have been explored.The methods used are interviews, observations, thematic analysis, and hierarchical task analysis. Thestudy’s major findings were the importance of clear communication and a good relationship betweenthe ship and shore organisation.

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  • 3067.
    Åhlen, Julia
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Color correction of underwater images based on estimation of diffuse attenuation coefficients2003In: Proceedings of 3rd conference for the promotion of research in IT, 2003Conference paper (Other scientific)
  • 3068.
    Åhlén, Julia
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Colour Correction of Underwater Images Using Spectral Data2005Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    For marine sciences sometimes there is a need to perform underwater photography. Optical properties of light cause severe quality problems for underwater photography. Light of different energies is absorbed at highly different rates under water causing significant bluishness of the images. If the colour dependent attenuation under water can be properly estimated it should be possible to use computerised image processing to colour correct digital images using Beer’s Law.

    In this thesis we have developed such estimation and correction methods that have become progressively more complicated and more accurate giving successively better correction results. A process of estimation of downwelling attenuation coefficients from multi or hyper spectral data is a basis for automatic colour restoration of underwater taken images. The results indicate that for each diving site the unique and precise coefficients can be obtained.

    All standard digital cameras have built in white balancing and colour enhancement functions designed to make the images as aesthetically pleasing as possible. These functions can in most cameras not be switched off and the algorithms used are proprietary and undocumented. However, these enhancement functions can be estimated. Applying their reverse creates un-enhanced images and we show that our algorithms for underwater colour correction works significantly better when applied to such images.

    Finally, we have developed a method that uses point spectra from the spectrometer together with RGB colour images from a camera to generate pseudo-hyper-spectral images. Each of these can then be colour corrected. Finally, the images can be weighted together in the proportions needed to create new correct RGB images. This method is somewhat computationally demanding but gives very encouraging results.

    The algorithms and applications presented in this thesis show that automatic colour correction of underwater images can increase the credibility of data taken underwater for marine scientific purposes.

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  • 3069. Åhlén, Julia
    et al.
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Automatic Water Body Extraction From Remote Sensing Images Using Entropy2015In: SGEM2015 Conference Proceedings, 2015, Vol. 2, p. 517-524Conference paper (Refereed)
    Abstract [en]

    This research focuses on automatic extraction of river banks and other inland waters from remote sensing images. There are no up to date accessible databases of rivers and most of other waters objects for modelling purposes. The main reason for that is that some regions are hard to access with the traditional ground through techniques and thus the boundary of river banks are uncertain in many geographical positions. The other reason is the limitations of widely applied method for extraction of water bodies called normalized-difference water index (NDWI). There is a novel approach to extract water bodies, which is based on pixel level variability or entropy, however, the methods work somewhat satisfactory on high spatial resolution images, there is no verification of the method performance on moderate or low resolution images. Problems encounter identification of mixed water pixels and e.g. roads, which are built in attachment to river banks and thus can be classified as rivers. In this work we propose an automatic extraction of river banks using image entropy, combined with NDWI identification. In this study only moderate spatial resolution Landsat TM are tested. Areas of interest include both major river banks and inland lakes. Calculating entropy on such poor spatial resolution images will lead to misinterpretation of water bodies, which all exhibits the same small variation of pixel values as e.g. some open or urban areas. Image entropy thus is calculated with the modification that involves the incorporation of local normalization index or variability coefficient. NDWI will produce an image where clear water exhibits large difference comparing to other land features. We are presenting an algorithm that uses an NDWI prior to entropy processing, so that bands used to calculate it, are chosen in clear connection to water body features that are clearly discernible.As a result we visualize a clear segmentation of the water bodies from the remote sensing images and verify the coordinates with a given geographic reference.

  • 3070.
    Åhlén, Julia
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Seipel, Stefan
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Computer science. Uppsala University, Department of Information Technology, Sweden .
    Automatic water body extraction from remote sensing images using entropy2015In: Proceedings of the International Multidisciplinary Scientific GeoConference SGEM, 2015, Vol. 4, p. 517-524Conference paper (Refereed)
    Abstract [en]

    This research focuses on automatic extraction of river banks and other inland waters from remote sensing images. There are no up to date accessible databases of rivers and most of other waters objects for modelling purposes. The main reason for that is that some regions are hard to access with the traditional ground through techniques and thus the boundary of river banks are uncertain in many geographical positions. The other reason is the limitations of widely applied method for extraction of water bodies called normalized-difference water index (NDWI). There is a novel approach to extract water bodies, which is based on pixel level variability or entropy, however, the methods work somewhat satisfactory on high spatial resolution images, there is no verification of the method performance on moderate or low resolution images. Problems encounter identification of mixed water pixels and e.g. roads, which are built in attachment to river banks and thus can be classified as rivers. In this work we propose an automatic extraction of river banks using image entropy, combined with NDWI identification. In this study only moderate spatial resolution Landsat TM are tested. Areas of interest include both major river banks and inland lakes. Calculating entropy on such poor spatial resolution images will lead to misinterpretation of water bodies, which all exhibits the same small variation of pixel values as e.g. some open or urban areas. Image entropy thus is calculated with the modification that involves the incorporation of local normalization index or variability coefficient. NDWI will produce an image where clear water exhibits large difference comparing to other land features. We are presenting an algorithm that uses an NDWI prior to entropy processing, so that bands used to calculate it, are chosen in clear connection to water body features that are clearly discernible.As a result we visualize a clear segmentation of the water bodies from the remote sensing images and verify the coordinates with a given geographic reference.

  • 3071.
    Åhlén, Julia
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Urban and regional planning/GIS-institute.
    Seipel, Stefan
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Computer science.
    Early Recognition of Smoke in Digital Video2010In: Advances in Communications, Computers, Systems, Circuits and Devices: European Conference of Systems, ECS'10, European Conference of Circuits Technology and Devices, ECCTD'10, European Conference of Communications, ECCOM'10, ECCS'10 / [ed] Mladenov, V; Psarris, K; Mastorakis, N; Caballero, A; Vachtsevanos, G, Athens: World Scientific and Engineering Academy and Society, 2010, p. 301-306Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for direct smoke detection from video without enhancement pre-processing steps. Smoke is characterized by transparency, gray color and irregularities in motion, which are hard to describe with the basic image features. A method for robust smoke description using a color balancing algorithm and turbulence calculation is presented in this work. Background extraction is used as a first step in processing. All moving objects are candidates for smoke. We make use of Gray World algorithm and compare the results with the original video sequence in order to extract image features within some particular gray scale interval. As a last step we calculate shape complexity of turbulent phenomena and apply it to the incoming video stream. As a result we extract only smoke from the video. Features such shadows, illumination changes and people will not be mistaken for smoke by the algorithm. This method gives an early indication of smoke in the observed scene.

  • 3072.
    Åhlén, Julia
    et al.
    Högskolan i Gävle.
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Knowledge Based Single Building Extraction and Recognition2014In: Proceedings WSEAS International Conference on Computer Engineering and Applications, 2014, 2014, p. 29-35Conference paper (Refereed)
    Abstract [en]

    Building facade extraction is the primary step in the recognition process in outdoor scenes. It is also achallenging task since each building can be viewed from different angles or under different lighting conditions. Inoutdoor imagery, regions, such as sky, trees, pavement cause interference for a successful building facade recognition.In this paper we propose a knowledge based approach to automatically segment out the whole facade or majorparts of the facade from outdoor scene. The found building regions are then subjected to recognition process. Thesystem is composed of two modules: segmentation of building facades region module and facade recognition module.In the facade segmentation module, color processing and objects position coordinates are used. In the facaderecognition module, Chamfer metrics are applied. In real time recognition scenario, the image with a building isfirst analyzed in order to extract the facade region, which is then compared to a database with feature descriptors inorder to find a match. The results show that the recognition rate is dependent on a precision of building extractionpart, which in turn, depends on a homogeneity of colors of facades.

  • 3073.
    Åhlén, Julia
    et al.
    Högskolan i Gävle.
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    TIME-SPACE VISUALISATION OF AMUR RIVER CHANNEL CHANGES DUE TO FLOODING DISASTER2014In: Proceedings of International Multidisciplinary Scientific GeoScience Conference (SGEM), 2014, 2014Conference paper (Refereed)
    Abstract [en]

    The analysis of flooding levels is a highly complex temporal and spatial assessment task that involves estimation of distances between references in geographical space as well as estimations of instances along the time-line that coincide with given spatial locations. This work has an aim to interactively explore changes of Amur River boundaries caused by the severe flooding in September 2013. In our analysis of river bank changes we use satellite imagery (Landsat 7) to extract parts belonging to Amur River. We use imagery from that covers time interval July 2003 until February 2014. Image data is pre-processed using low level image processing techniques prior to visualization. Pre-processing has a purpose to extract information about the boundaries of the river, and to transform it into a vectorized format, suitable as inputs subsequent visualization. We develop visualization tools to explore the spatial and temporal relationship in the change of river banks. In particular the visualization shall allow for exploring specific geographic locations and their proximity to the river/floods at arbitrary times. We propose a time space visualization that emanates from edge detection, morphological operations and boundary statistics on Landsat 2D imagery in order to extract the borders of Amur River. For the visualization we use the time-spacecube metaphor. It is based on a 3D rectilinear context, where the 2D geographical coordinate system is extended with a time-axis pointing along the 3rd Cartesian axis. Such visualization facilitates analysis of the channel shape of Amur River and thus enabling for a conclusion regarding the defined problem. As a result we demonstrate our time-space visualization for river Amur and using some amount of geographical point data as a reference we suggest an adequate method of interpolation or imputation that can be employed to estimate value at a given location and time.

  • 3074.
    Åhlén, Julia
    et al.
    Högskolan i Gävle.
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Liu, Fei
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Evaluation of the Automatic methods for Building Extraction2014In: International Journal of Computers and Communications, ISSN 2074-1294, Vol. 8, p. 171-176Article in journal (Refereed)
  • 3075.
    Åhlén Julia, Sundgren David
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bottom Reflectance Influence on a Color Correction Algorithm for Underwater Images2003In: 13th Scandinavian Conference, SCIA 2003 Göteborg, Sweden, June 29-July 2, 2003, 2003, p. 922-926Conference paper (Refereed)
  • 3076.
    Åhlén, Julia
    et al.
    Uppsala universitet.
    Sundgren, David
    Stockholms universitet.
    Bottom Reflectance Influence on a Color Correction Algorithm for Underwater Images2003In: Proceedings of the 13th Scandinavian Conference on Image Analysis / [ed] Bigun, J., Gustavsson, T., Berlin: Springer , 2003, p. 922-926Conference paper (Refereed)
    Abstract [en]

    Diminishing the negative effects of water column introduced on digital underwater images is the aim of a color correction algorithm presented by the authors in a previous paper. The present paper describes an experimental result and set of calculations for determining the impact of bottom reflectance on the algorithm's performance. This concept is based on the estimation of the relative reflectance of various bottom types such as sand, bleached corals and algae. We describe the adverse effects of extremely low and high bottom reflectances on the algorithm.

  • 3077.
    Åhlén, Julia
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Sundgren, David
    KTH.
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Pre-Processing of Underwater Images Taken in shallow Water for Color Reconstruction Purposes2005In: IASTED Proceeding (479): IASTED 7th Conference on Signal and Image Processing - 2005, 2005Conference paper (Refereed)
    Abstract [en]

    Coral reefs are monitored with different techniques in or der to examine their health. Digital cameras, which pro vide an economically defendable tool for marine scientists to collect underwater data, tend to produce bluish images due to severe absorption of light at longer wavelengths. In this paper we study the possibilities of correcting for this color distortion through image processing. The decrease of red light by depth can be predicted by Beer’s Law. An other parameter that has been taken into account is the image enhancement functions built into the camera. We use a spectrometer and a reflectance standard to obtain the data needed to approximate the joint effect of these func tions. This model is used to pre-process the underwater images taken by digital cameras so that the red, green and blue channels show correct values before the images are subjected to correction for the effects of the water column through application of Beer’s Law. This process is fully automatic and the amount of processed images is limited only by the speed of computer system. Experimental re sults show that the proposed method works well for cor recting images taken at different depths with two different cameras.

  • 3078.
    Åhlén, Julia
    et al.
    University of Gävle, Department of Mathematics, Natural and Computer Sciences, Ämnesavdelningen för datavetenskap.
    Sundgren, David
    University of Gävle, Department of Mathematics, Natural and Computer Sciences, Ämnesavdelningen för matematik och statistik.
    Bengtsson, Ewert
    Pre-Processing of Underwater Images Taken in Shallow Waters for Color Reconstruction Purposes2005In: Proceedings of the 7th IASTED International Conference on Signal and Image Processing, 2005Conference paper (Refereed)
    Abstract [en]

    Coral reefs are monitored with different techniques in order to examine their health. Digital cameras, which provide an economically defendable tool for marine scientists to collect underwater data, tend to produce bluish images due to severe absorption of light at longer wavelengths. In this paper we study the possibilities of correcting for this color distortion through image processing. The decrease of red light by depth can be predicted by Beer's law. Another parameter that has to be taken into account is the image enhancement functions built into the camera. We use a spectrometer and a reflectance standard to obtain the data needed to approximate the joint effect of these functions. This model is used to pre-process the underwater images taken by digital cameras so that the red, green and blue channels show correct values before the images are subjected to correction for the effects of water column through application of Beer's law. This process is fully automatic and the amount of processed images is limited only by the speed of computer system. Experimental results show that the proposed method works well for correcting images taken at different depths with two different cameras.

  • 3079.
    Åhlén, Julia
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Sundgren, David
    KTH.
    Lindell, Tommy
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Dissolved Organic Matters Impact on Colour2005In: Image Analysis: 14th Scandinavian Conference, SCIA 2005, 2005, p. 1148-1156Conference paper (Refereed)
    Abstract [en]

    The natural properties of water column usually affect under-water

    imagery by suppressing high-energy light. In application such as

    color correction of underwater images estimation of water column parameters is crucial. Diffuse attenuation coefficients are estimated and used for further processing of underwater taken data. The coefficients will give information on how fast light of different wavelengths decreases with increasing depth. Based on the exact depth measurements and data from a spectrometer the calculation of downwelling irradiance will be done. Chlorophyll concentration and a yellow substance factor contribute to a great variety of values of attenuation coefficients at different depth. By taking advantage of variations in depth, a method is presented to

    estimate the in uence of dissolved organic matters and chlorophyll on color correction. Attenuation coefficients that depends on concentration of dissolved organic matters in water gives an indication on how well any spectral band is suited for color correction algorithm.

  • 3080.
    Åkerlind, Christina
    et al.
    Linköping University, Department of Physics, Chemistry and Biology. Linköping University, Faculty of Science & Engineering. FOI, Linköping, Sweden.
    Fagerström, Jan
    FOI, Linköping, Sweden.
    Hallberg, Tomas
    FOI, Linköping, Sweden.
    Kariis, Hans
    FOI, Linköping, Sweden.
    Evaluation criteria for spectral design of camouflage2015In: Proc. SPIE 9653, Target and Background Signatures / [ed] Karin U. Stein; Ric H. M. A. Schleijpen, SPIE - International Society for Optical Engineering, 2015, Vol. 9653, p. Art.no: 9653-2-Conference paper (Refereed)
    Abstract [en]

    In development of visual (VIS) and infrared (IR) camouflage for signature management, the aim is the design of surface properties of an object to spectrally match or adapt to a background and thereby minimizing the contrast perceived by a threatening sensor. The so called 'ladder model" relates the requirements for task measure of effectiveness with surface structure properties through the steps signature effectiveness and object signature. It is intended to link materials properties via platform signature to military utility and vice versa. Spectral design of a surface intends to give it a desired wavelength dependent optical response to fit a specific application of interest. Six evaluation criteria were stated, with the aim to aid the process to put requirement on camouflage and for evaluation. The six criteria correspond to properties such as reflectance, gloss, emissivity, and degree of polarization as well as dynamic properties, and broadband or multispectral properties. These criteria have previously been exemplified on different kinds of materials and investigated separately. Anderson and Åkerlind further point out that the six criteria rarely were considered or described all together in one and same publication previously. The specific level of requirement of the different properties must be specified individually for each specific situation and environment to minimize the contrast between target and a background. The criteria or properties are not totally independent of one another. How they are correlated is part of the theme of this paper. However, prioritization has been made due to the limit of space. Therefore all of the interconnections between the six criteria will not be considered in the work of this report. The ladder step previous to digging into the different material composition possibilities and choice of suitable materials and structures (not covered here), includes the object signature and decision of what the spectral response should be, when intended for a specific environment. The chosen spectral response should give a low detection probability (DP). How detection probability connects to image analysis tools and implementation of the six criteria is part of this work.

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  • 3081.
    Åkerström, Ulrika
    Linköping University, Department of Computer and Information Science, Human-Centered systems.
    Lekmannabedömning av ett självkörande fordons körförmåga: betydelsen av att erfara fordonet i trafiken2022Independent thesis Basic level (degree of Bachelor), 12 credits / 18 HE creditsStudent thesis
    Abstract [sv]

    Datorstyrda maskiner som både kan styra sina egna aktiviteter och som har ett stort rörelseomfång kommer snart att dela vår fysiska miljö vilket kommer innebära en drastisk förändring för vår nuvarande mänskliga kontext. Tidigare olyckor som skett mellan mänskliga förare och automatiserade fordon kan förklaras genom en bristande förståelse för de automatiserade fordonets beteende. Det är därför viktigt att ta reda på hur människor förstår automatiserade fordons förmågor och begränsningar. SAE International, en global yrkeskår får ingenjörer verksamma inom fordonsindustrin, har definierat ett ramverk som beskriver funktionaliteten hos automatiserade fordon i 6 olika nivåer. Den rapporterade studien undersökte med utgångspunkt i detta ramverk vilken automationsgrad deltagarna antar att en självkörande buss har genom deltagarnas upplevelse av fordonet. Inom ramarna för studien färdades deltagarna en kort sträcka på en självkörande buss och besvarade en enkät om hur de ser på bussens förmågor och begränsningar både före och efter färden. Studieresultatet visade att hälften av deltagarna överskattade bussens automationsgrad. Efter att ha färdats med bussen justerade deltagarna ner sina förväntningar på fordonets körförmåga vilket stämde bättre överens med bussens förmågor och begränsningar. Deltagarna rapporterade även att de var mer säkra i sina bedömningar efter erfarenhet av fordonet. Sammanfattningsvis tyder resultatet på att (1) människor tenderar att överskatta automatiserade fordons körförmåga, men att (2) deras uppfattning justeras i samband med att de kommer i kontakt med det automatiserade fordonet i verkligheten och att (3) de då även blir mer säkra i sina bedömningar. Detta borde tas i beaktning vid utveckling av självkörande fordon för att minska risken för olyckor i trafiken.

     

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  • 3082.
    Åkesson, Ulrik
    Mälardalen University, School of Innovation, Design and Engineering.
    Design of a multi-camera system for object identification, localisation, and visual servoing2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this thesis, the development of a stereo camera system for an intelligent tool is presented. The task of the system is to identify and localise objects so that the tool can guide a robot. Different approaches to object detection have been implemented and evaluated and the systems ability to localise objects has been tested. The results show that the system can achieve a localisation accuracy below 5 mm.

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  • 3083.
    Åslund, Conrad
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Matching handwritten notes using computer vision and pattern matching2022Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    What people take for granted is not as easy for computers. Being able tojudge whether an image is the same even though it has a differentresolution or is taken from a different angle or light condition is easyfor humans but much more difficult for computers. Today’s mobiles aremore powerful than ever, which has opened up for more hardware-demandingalgorithms to be processed. How to effectively match handwritten notesto eliminate duplicates in an application. Are there better or worsemethods and approaches, and how do they compare to each other? Can youachieve both accuracy and speed? By analyzing images taken at differentangles, distances, and lighting conditions, different methods andapproaches have been developed and analyzed. The methods are representedin various tables where time and accuracy are represented. Eightdifferent methods were evaluated. The methods were tuned on one datasetconsisting of 150 post-it notes, each imaged under four conditions,leading to 600 images and 1800 possible pair-wise matches. The methodswere thereafter evaluated on an independent dataset consisting of 250post-it notes, each imaged under four conditions, leading to 1000 imagesand 3000 possible pair-wise matches. The best method found 99.7%, andthe worst method found 62.9% of the matching pairs. Seven of the eightevaluated matches did not make any incorrect matches.

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  • 3084.
    Åstrand, Björn
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Baerveldt, Albert-Jan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    A mobile robot for mechanical weed control2003In: International Sugar Journal, ISSN 0020-8841, Vol. 105, no 1250, p. 89-95Article in journal (Refereed)
    Abstract [en]

    This paper presents an autonomous agricultural mobile robot for mechanical weed control in outdoor environments. The robot employs two vision systems: one grey-level vision system that is able to recognise the row structure formed by the crops and to guide the robot along the rows and a second, colour-based vision system that is able to identify a single crop among weed plants. This vision system controls a weeding-tool that removes the weed within the row of crops. It has been shown that colour vision is feasible for single plant identification, i.e. discriminating between crops and weeds. The system as a whole has been verified, showing that the subsystems are able to work together effectively. A first trial in a greenhouse showed that the robot is able to manage weed control within a row of sugar beet plants.

  • 3085.
    Åstrand, Björn
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Bouguerra, Abdelbaki
    Learning Systems Lab (AASS), Dept. of Technology, Örebro University, Sweden.
    Andreasson, Henrik
    Learning Systems Lab (AASS), Dept. of Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Learning Systems Lab (AASS), Dept. of Technology, Örebro University, Sweden.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    An autonomous robotic system for load transportation2009In: Program and Abstracts, Fourth Swedish Workshop on Autonomous Robotics, SWAR'09 / [ed] Lars Asplund, Västerås: Mälardalen University , 2009, p. 56-57Conference paper (Other academic)
  • 3086.
    Åström, Anders
    et al.
    Combitech AB, Linköping, Sweden.
    Forchheimer, Robert
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, Faculty of Science & Engineering.
    Fast, low-complex, non-contact motion encoder based on the NSIP concept2017In: IS and T International Symposium on Electronic Imaging Science and Technology, Society for Imaging Science and Technology , 2017, p. 91-95Conference paper (Refereed)
    Abstract [en]

    We describe the implementation of a non-contact motion encoder based on the Near-Sensor Image Processing (NSIP) concept. Rather than computing image displacements between frames we search for LEP stability as used successfully in a previously published Time-to-Impact detector. A LEP is a single pixel feature that is tracked during its motion. It is found that this results in a non-complex and fast implementation. As with other NSIP-based solutions, high dynamic range is obtained as the sensor adapts itself to the lighting conditions. © 2017, Society for Imaging Science and Technology.

  • 3087.
    Åström, Freddie
    et al.
    Heidelberg University, Germany.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Baravdish, George
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Mapping-Based Image Diffusion2017In: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, Vol. 57, no 3, p. 293-323Article in journal (Refereed)
    Abstract [en]

    In this work, we introduce a novel tensor-based functional for targeted image enhancement and denoising. Via explicit regularization, our formulation incorporates application-dependent and contextual information using first principles. Few works in literature treat variational models that describe both application-dependent information and contextual knowledge of the denoising problem. We prove the existence of a minimizer and present results on tensor symmetry constraints, convexity, and geometric interpretation of the proposed functional. We show that our framework excels in applications where nonlinear functions are present such as in gamma correction and targeted value range filtering. We also study general denoising performance where we show comparable results to dedicated PDE-based state-of-the-art methods.

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  • 3088.
    Åström, Freddie
    et al.
    Heidelberg Collaboratory for Image Processing Heidelberg University Heidelberg, Germany.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Scharr, Hanno
    BG-2: Plant Sciences Forschungszentrum Jülich 52425, Jülich, Germany.
    Adaptive sharpening of multimodal distributions2015In: Colour and Visual Computing Symposium (CVCS), 2015 / [ed] Marius Pedersen and Jean-Baptiste Thomas, IEEE , 2015Conference paper (Refereed)
    Abstract [en]

    In this work we derive a novel framework rendering measured distributions into approximated distributions of their mean. This is achieved by exploiting constraints imposed by the Gauss-Markov theorem from estimation theory, being valid for mono-modal Gaussian distributions. It formulates the relation between the variance of measured samples and the so-called standard error, being the standard deviation of their mean. However, multi-modal distributions are present in numerous image processing scenarios, e.g. local gray value or color distributions at object edges, or orientation or displacement distributions at occlusion boundaries in motion estimation or stereo. Our method not only aims at estimating the modes of these distributions together with their standard error, but at describing the whole multi-modal distribution. We utilize the method of channel representation, a kind of soft histogram also known as population codes, to represent distributions in a non-parametric, generic fashion. Here we apply the proposed scheme to general mono- and multimodal Gaussian distributions to illustrate its effectiveness and compliance with the Gauss-Markov theorem.

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  • 3089.
    Öfjäll, Kristoffer
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Adaptive Supervision Online Learning for Vision Based Autonomous Systems2016Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Driver assistance systems in modern cars now show clear steps towards autonomous driving and improvements are presented in a steady pace. The total number of sensors has also decreased from the vehicles of the initial DARPA challenge, more resembling a pile of sensors with a car underneath. Still, anyone driving a tele-operated toy using a video link is a demonstration that a single camera provides enough information about the surronding world.  

    Most lane assist systems are developed for highway use and depend on visible lane markers. However, lane markers may not be visible due to snow or wear, and there are roads without lane markers. With a slightly different approach, autonomous road following can be obtained on almost any kind of road. Using realtime online machine learning, a human driver can demonstrate driving on a road type unknown to the system and after some training, the system can seamlessly take over. The demonstrator system presented in this work has shown capability of learning to follow different types of roads as well as learning to follow a person. The system is based solely on vision, mapping camera images directly to control signals.  

    Such systems need the ability to handle multiple-hypothesis outputs as there may be several plausible options in similar situations. If there is an obstacle in the middle of the road, the obstacle can be avoided by going on either side. However the average action, going straight ahead, is not a viable option. Similarly, at an intersection, the system should follow one road, not the average of all roads.  

    To this end, an online machine learning framework is presented where inputs and outputs are represented using the channel representation. The learning system is structurally simple and computationally light, based on neuropsychological ideas presented by Donald Hebb over 60 years ago. Nonetheless the system has shown a cabability to learn advanced tasks. Furthermore, the structure of the system permits a statistical interpretation where a non-parametric representation of the joint distribution of input and output is generated. Prediction generates the conditional distribution of the output, given the input.  

    The statistical interpretation motivates the introduction of priors. In cases with multiple options, such as at intersections, a prior can select one mode in the multimodal distribution of possible actions. In addition to the ability to learn from demonstration, a possibility for immediate reinforcement feedback is presented. This allows for a system where the teacher can choose the most appropriate way of training the system, at any time and at her own discretion.  

    The theoretical contributions include a deeper analysis of the channel representation. A geometrical analysis illustrates the cause of decoding bias commonly present in neurologically inspired representations, and measures to counteract it. Confidence values are analyzed and interpreted as evidence and coherence. Further, the use of the truncated cosine basis function is motivated.  

    Finally, a selection of applications is presented, such as autonomous road following by online learning and head pose estimation. A method founded on the same basic principles is used for visual tracking, where the probabilistic representation of target pixel values allows for changes in target appearance.

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    Supplementary files with videos
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    presentationsbild
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    Channel vector curves, Four channels, 3D space
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    Channel vector curves, Five channels, 4D space
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    Channel vector curves, Seven channels, 6D space
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    Cone, Three channels, 3D space
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    Cone, Four channels, 4D space
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    Cone, Seven channels, 7D space
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    Associative learning illustration
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    Decoding of five pixels in a sequence
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    Sequence with translating cameraman image
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    Video from UAV
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    Original video from the UAV
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    Autonomous Road Following Application, Use case demo
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    Autonomous Road Following Application, Demonstrator system
  • 3090.
    Öfjäll, Kristoffer
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    LEAP, A Platform for Evaluation of Control Algorithms2010Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Most people are familiar with the BRIO labyrinth game and the challenge of guiding the ball through the maze. The goal of this project was to use this game to create a platform for evaluation of control algorithms. The platform was used to evaluate a few different controlling algorithms, both traditional automatic control algorithms as well as algorithms based on online incremental learning.

    The game was fitted with servo actuators for tilting the maze. A camera together with computer vision algorithms were used to estimate the state of the game. The evaluated controlling algorithm had the task of calculating a proper control signal, given the estimated state of the game.

    The evaluated learning systems used traditional control algorithms to provide initial training data. After initial training, the systems learned from their own actions and after a while they outperformed the controller used to provide initial training.

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  • 3091.
    Öfjäll, Kristoffer
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Online Learning for Robot Vision2014Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In tele-operated robotics applications, the primary information channel from the robot to its human operator is a video stream. For autonomous robotic systems however, a much larger selection of sensors is employed, although the most relevant information for the operation of the robot is still available in a single video stream. The issue lies in autonomously interpreting the visual data and extracting the relevant information, something humans and animals perform strikingly well. On the other hand, humans have great diculty expressing what they are actually looking for on a low level, suitable for direct implementation on a machine. For instance objects tend to be already detected when the visual information reaches the conscious mind, with almost no clues remaining regarding how the object was identied in the rst place. This became apparent already when Seymour Papert gathered a group of summer workers to solve the computer vision problem 48 years ago [35].

    Articial learning systems can overcome this gap between the level of human visual reasoning and low-level machine vision processing. If a human teacher can provide examples of what to be extracted and if the learning system is able to extract the gist of these examples, the gap is bridged. There are however some special demands on a learning system for it to perform successfully in a visual context. First, low level visual input is often of high dimensionality such that the learning system needs to handle large inputs. Second, visual information is often ambiguous such that the learning system needs to be able to handle multi modal outputs, i.e. multiple hypotheses. Typically, the relations to be learned  are non-linear and there is an advantage if data can be processed at video rate, even after presenting many examples to the learning system. In general, there seems to be a lack of such methods.

    This thesis presents systems for learning perception-action mappings for robotic systems with visual input. A range of problems are discussed, such as vision based autonomous driving, inverse kinematics of a robotic manipulator and controlling a dynamical system. Operational systems demonstrating solutions to these problems are presented. Two dierent approaches for providing training data are explored, learning from demonstration (supervised learning) and explorative learning (self-supervised learning). A novel learning method fullling the stated demands is presented. The method, qHebb, is based on associative Hebbian learning on data in channel representation. Properties of the method are demonstrated on a vision-based autonomously driving vehicle, where the system learns to directly map low-level image features to control signals. After an initial training period, the system seamlessly continues autonomously. In a quantitative evaluation, the proposed online learning method performed comparably with state of the art batch learning methods.

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    Online Learning for Robot Vision
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  • 3092.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Biologically Inspired Online Learning of Visual Autonomous Driving2014In: Proceedings British Machine Vision Conference 2014 / [ed] Michel Valstar; Andrew French; Tony Pridmore, BMVA Press , 2014, p. 137-156Conference paper (Refereed)
    Abstract [en]

    While autonomously driving systems accumulate more and more sensors as well as highly specialized visual features and engineered solutions, the human visual system provides evidence that visual input and simple low level image features are sufficient for successful driving. In this paper we propose extensions (non-linear update and coherence weighting) to one of the simplest biologically inspired learning schemes (Hebbian learning). We show that this is sufficient for online learning of visual autonomous driving, where the system learns to directly map low level image features to control signals. After the initial training period, the system seamlessly continues autonomously. This extended Hebbian algorithm, qHebb, has constant bounds on time and memory complexity for training and evaluation, independent of the number of training samples presented to the system. Further, the proposed algorithm compares favorably to state of the art engineered batch learning algorithms.

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  • 3093.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Combining Vision, Machine Learning and Automatic Control to Play the Labyrinth Game2012In: Proceedings of SSBA, Swedish Symposium on Image Analysis, 2012, 2012Conference paper (Other academic)
    Abstract [en]

    The labyrinth game is a simple yet challenging platform, not only for humans but also for control algorithms and systems. The game is easy to understand but still very hard to master. From a system point of view, the ball behavior is in general easy to model but close to the obstacles there are severe non-linearities. Additionally, the far from flat surface on which the ball rolls provides for changing dynamics depending on the ball position.

    The general dynamics of the system can easily be handled by traditional automatic control methods. Taking the obstacles and uneven surface into account would require very detailed models of the system. A simple deterministic control algorithm is combined with a learning control method. The simple control method provides initial training data. As thelearning method is trained, the system can learn from the results of its own actions and the performance improves well beyond the performance of the initial controller.

    A vision system and image analysis is used to estimate the ball position while a combination of a PID controller and a learning controller based on LWPR is used to learn to steer the ball through the maze.

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  • 3094.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Integrating Learning and Optimization for Active Vision Inverse Kinematics2013In: Proceedings of SSBA, Swedish Symposium on Image Analysis, 2013, 2013Conference paper (Other academic)
  • 3095.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Online Learning and Mode Switching for Autonomous Driving from Demonstration2014In: Proceedings of SSBA, Swedish Symposium on Image Analysis, 2014, 2014Conference paper (Other academic)
  • 3096.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Online learning of autonomous driving using channel representations of multi-modal joint distributions2015In: Proceedings of SSBA, Swedish Symposium on Image Analysis, 2015, Swedish Society for automated image analysis , 2015Conference paper (Other academic)
  • 3097.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Online Learning of Vision-Based Robot Control during Autonomous Operation2015In: New Development in Robot Vision / [ed] Yu Sun, Aman Behal and Chi-Kit Ronald Chung, Springer Berlin/Heidelberg, 2015, p. 137-156Chapter in book (Refereed)
    Abstract [en]

    Online learning of vision-based robot control requires appropriate activation strategies during operation. In this chapter we present such a learning approach with applications to two areas of vision-based robot control. In the first setting, selfevaluation is possible for the learning system and the system autonomously switches to learning mode for producing the necessary training data by exploration. The other application is in a setting where external information is required for determining the correctness of an action. Therefore, an operator provides training data when required, leading to an automatic mode switch to online learning from demonstration. In experiments for the first setting, the system is able to autonomously learn the inverse kinematics of a robotic arm. We propose improvements producing more informative training data compared to random exploration. This reduces training time and limits learning to regions where the learnt mapping is used. The learnt region is extended autonomously on demand. In experiments for the second setting, we present an autonomous driving system learning a mapping from visual input to control signals, which is trained by manually steering the robot. After the initial training period, the system seamlessly continues autonomously. Manual control can be taken back at any time for providing additional training.

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  • 3098.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Weighted Update and Comparison for Channel-Based Distribution Field Tracking2015In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II, Springer, 2015, Vol. 8926, p. 218-231Conference paper (Refereed)
    Abstract [en]

    There are three major issues for visual object trackers: modelrepresentation, search and model update. In this paper we address thelast two issues for a specic model representation, grid based distributionmodels by means of channel-based distribution elds. Particularly weaddress the comparison part of searching. Previous work in the areahas used standard methods for comparison and update, not exploitingall the possibilities of the representation. In this work we propose twocomparison schemes and one update scheme adapted to the distributionmodel. The proposed schemes signicantly improve the accuracy androbustness on the Visual Object Tracking (VOT) 2014 Challenge dataset.

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  • 3099.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Robinson, Andreas
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Visual Autonomous Road Following by Symbiotic Online Learning2016In: Intelligent Vehicles Symposium (IV), 2016 IEEE, 2016, p. 136-143Conference paper (Refereed)
    Abstract [en]

    Recent years have shown great progress in driving assistance systems, approaching autonomous driving step by step. Many approaches rely on lane markers however, which limits the system to larger paved roads and poses problems during winter. In this work we explore an alternative approach to visual road following based on online learning. The system learns the current visual appearance of the road while the vehicle is operated by a human. When driving onto a new type of road, the human driver will drive for a minute while the system learns. After training, the human driver can let go of the controls. The present work proposes a novel approach to online perception-action learning for the specific problem of road following, which makes interchangeably use of supervised learning (by demonstration), instantaneous reinforcement learning, and unsupervised learning (self-reinforcement learning). The proposed method, symbiotic online learning of associations and regression (SOLAR), extends previous work on qHebb-learning in three ways: priors are introduced to enforce mode selection and to drive learning towards particular goals, the qHebb-learning methods is complemented with a reinforcement variant, and a self-assessment method based on predictive coding is proposed. The SOLAR algorithm is compared to qHebb-learning and deep learning for the task of road following, implemented on a model RC-car. The system demonstrates an ability to learn to follow paved and gravel roads outdoors. Further, the system is evaluated in a controlled indoor environment which provides quantifiable results. The experiments show that the SOLAR algorithm results in autonomous capabilities that go beyond those of existing methods with respect to speed, accuracy, and functionality. 

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    Visual Autonomous Road Following by Symbiotic Online Learning
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  • 3100.
    Öfverstedt, Johan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Methods for Reliable Image Registration: Algorithms, Distance Measures, and Representations2022Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Much biomedical and medical research relies on the collection of ever-larger amounts of image data (both 2D images and 3D volumes, as well as time-series) and increasingly from multiple sources. Image registration, the process of finding correspondences between images based on the affinity of features of interest, is often required as a vital step towards the final analysis, which may consist of a comparison of images, measurement of movement, or fusion of complementary information. The contributions in this work are centered around reliable image registration methods for both 2D and 3D images with the aim of wide applicability: similarity and distance measures between images for image registration, algorithms for efficient computation of these, and other commonly used measures for both local and global optimization frameworks, and representations for multimodal image registration where the appearance and structures present in the images may vary dramatically.

    The main contributions are: (i) distance measures for affine symmetric intensity image registration, combining intensity and spatial information based on the notion of alpha-cuts from fuzzy set theory; (ii) the extension of the affine registration method to more flexible deformable transformation models, leading to the framework Intensity and Spatial Information-Based Deformable Image Registration (INSPIRE); (iii) two efficient algorithms for computing the proposed distances and their spatial gradients and thereby enabling local gradient-based optimization; (iv) a contrastive representation learning method, Contrastive Multimodal Image Representation for Registration (CoMIR), utilizing deep learning techniques to obtain common representations that can be registered using methods designed for monomodal scenarios; (v) efficient algorithms for global optimization of mutual information and similarities of normalized gradient fields; (vi) a comparative study exploring the applicability of modern image-to-image translation methods to facilitate multimodal registration; (vii) the Stochastic Distance Transform, using the theory of discrete random sets to offer improved noise-insensitivity to distance computations; (viii) extensive evaluation of the proposed image registration methods on a number of different datasets mainly from (bio)medical imaging, where they exhibit excellent performance, and reliability, suggesting wide utility.

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