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  • 1851.
    Öfjäll, Kristoffer
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Online Learning for Robot Vision2014Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    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.

  • 1852.
    Öfjäll, Kristoffer
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Biologically Inspired Online Learning of Visual Autonomous Driving2014Ingår i: Proceedings British Machine Vision Conference 2014 / [ed] Michel Valstar; Andrew French; Tony Pridmore, BMVA Press , 2014, s. 137-156Konferensbidrag (Refereegranskat)
    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.

  • 1853.
    Öfjäll, Kristoffer
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Combining Vision, Machine Learning and Automatic Control to Play the Labyrinth Game2012Ingår i: Proceedings of SSBA, Swedish Symposium on Image Analysis, 2012, 2012Konferensbidrag (Övrigt vetenskapligt)
    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.

  • 1854.
    Öfjäll, Kristoffer
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Integrating Learning and Optimization for Active Vision Inverse Kinematics2013Ingår i: Proceedings of SSBA, Swedish Symposium on Image Analysis, 2013, 2013Konferensbidrag (Övrigt vetenskapligt)
  • 1855.
    Öfjäll, Kristoffer
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Online Learning and Mode Switching for Autonomous Driving from Demonstration2014Ingår i: Proceedings of SSBA, Swedish Symposium on Image Analysis, 2014, 2014Konferensbidrag (Övrigt vetenskapligt)
  • 1856.
    Öfjäll, Kristoffer
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Online learning of autonomous driving using channel representations of multi-modal joint distributions2015Ingår i: Proceedings of SSBA, Swedish Symposium on Image Analysis, 2015, Swedish Society for automated image analysis , 2015Konferensbidrag (Övrigt vetenskapligt)
  • 1857.
    Öfjäll, Kristoffer
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Online Learning of Vision-Based Robot Control during Autonomous Operation2015Ingår i: New Development in Robot Vision / [ed] Yu Sun, Aman Behal and Chi-Kit Ronald Chung, Springer Berlin/Heidelberg, 2015, s. 137-156Kapitel i bok, del av antologi (Refereegranskat)
    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.

  • 1858.
    Öfjäll, Kristoffer
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Weighted Update and Comparison for Channel-Based Distribution Field Tracking2015Ingår i: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II, Springer, 2015, Vol. 8926, s. 218-231Konferensbidrag (Refereegranskat)
    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.

  • 1859.
    Öfjäll, Kristoffer
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Robinson, Andreas
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Visual Autonomous Road Following by Symbiotic Online Learning2016Ingår i: Intelligent Vehicles Symposium (IV), 2016 IEEE, 2016, s. 136-143Konferensbidrag (Refereegranskat)
    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. 

  • 1860.
    Öfverstedt, Johan
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Lindblad, Joakim
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sladoje, Natasa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Stochastic Distance Functions with Applications in Object Detection and Image Segmentation2019Ingår i: Swedish Symposium on Image Analysis, 2019Konferensbidrag (Övrigt vetenskapligt)
  • 1861.
    Öfverstedt, Johan
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Lindblad, Joakim
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sladoje, Natasa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Stochastic Distance Transform2019Ingår i: Discrete Geometry for Computer Imagery, Springer, 2019, s. 75-86Konferensbidrag (Refereegranskat)
    Abstract [en]

    The distance transform (DT) and its many variations are ubiquitous tools for image processing and analysis. In many imaging scenarios, the images of interest are corrupted by noise. This has a strong negative impact on the accuracy of the DT, which is highly sensitive to spurious noise points. In this study, we consider images represented as discrete random sets and observe statistics of DT computed on such representations. We, thus, define a stochastic distance transform (SDT), which has an adjustable robustness to noise. Both a stochastic Monte Carlo method and a deterministic method for computing the SDT are proposed and compared. Through a series of empirical tests, we demonstrate that the SDT is effective not only in improving the accuracy of the computed distances in the presence of noise, but also in improving the performance of template matching and watershed segmentation of partially overlapping objects, which are examples of typical applications where DTs are utilized.

  • 1862.
    Öfverstedt, Johan
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sladoje, Natasa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Lindblad, Joakim
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Distance between vector-valued fuzzy sets based on intersection decomposition with applications in object detection2017Ingår i: Mathematical Morphology and its Applications to Signal and Image Processing, Springer, 2017, Vol. 10225, s. 395-407Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present a novel approach to measuring distance between multi-channel images, suitably represented by vector-valued fuzzy sets. We first apply the intersection decomposition transformation, based on fuzzy set operations, to vector-valued fuzzy representations to enable preservation of joint multi-channel properties represented in each pixel of the original image. Distance between two vector-valued fuzzy sets is then expressed as a (weighted) sum of distances between scalar-valued fuzzy components of the transformation. Applications to object detection and classification on multi-channel images and heterogeneous object representations are discussed and evaluated subject to several important performance metrics. It is confirmed that the proposed approach outperforms several alternative single-and multi-channel distance measures between information-rich image/ object representations.

  • 1863.
    Öfverstedt, Johan
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sladoje, Natasa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Lindblad, Joakim
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Distance Between Vector-valued Images based on Intersection Decomposition with Applications in Object Detection2018Ingår i: Swedish Symposium on Image Analysis, 2018Konferensbidrag (Övrigt vetenskapligt)
  • 1864.
    Ögren, Petter
    et al.
    Mech. & Aerosp. Eng. Dept., Princeton Univ., NJ, USA.
    Leonard, Naomi Ehrich
    Mech. & Aerosp. Eng. Dept., Princeton Univ., NJ, USA.
    A Convergent Dynamic Window Approach to Obstacle Avoidance2005Ingår i: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 21, nr 2, s. 188-195Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The dynamic window approach (DWA) is a well-known navigation scheme developed by Fox et al. and extended by Brock and Khatib. It is safe by construction, and has been shown to perform very efficiently in experimental setups. However, one can construct examples where the proposed scheme fails to attain the goal configuration. What has been lacking is a theoretical treatment of the algorithm's convergence properties. Here we present such a treatment by merging the ideas of the DWA with the convergent, but less performance-oriented, scheme suggested by Rimon and Koditschek. Viewing the DWA as a model predictive control (MPC) method and using the control Lyapunov function (CLF) framework of Rimon and Koditschek, we draw inspiration from an MPC/CLF framework put forth by Primbs to propose a version of the DWA that is tractable and convergent.

  • 1865.
    Ögren, Petter
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Robinson, John W.C.
    Swedish Defence Research Agency (FOI), Department of Aeronautics .
    A Model Based Approach to Modular Multi-Objective Robot Control2011Ingår i: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 63, nr 2, s. 257-282Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Two broad classes of robot controllers are the modular, and the model based approaches. The modular approaches include the Reactive or Behavior Based designs. They do not rely on mathematical system models, but are easy to design, modify and extend. In the model based approaches, a model is used to design a single controller with verifiable system properties. The resulting designs are however often hard to extend, without jeopardizing the previously proven properties. This paper describes an attempt to narrow the gap between the flexibility of the modular approaches, and the predictability of the model based approaches, by proposing a modular design that does the combination, or arbitration, of the different modules in a model based way. By taking the (model based) time derivatives of scalar, Lyapunov-like, objective functions into account, the arbitration module can keep track of the time evolution of the objectives. This enables it to handle objective tradeoffs in a predictable way by finding controls that preserve an important objective that is currently met, while striving to satisfy another, less important one that is not yet achieved. To illustrate the approach a UAV control problem from the literature is solved, resulting in comparable, or better, performance.

  • 1866.
    Ögren, Petter
    et al.
    Department of Autonomous Systems Swedish Defence Research Agency.
    Winstrand, Maja
    Minimizing Mission Risk in Fuel Constrained UAV Path Planning2008Ingår i: Journal of Guidance Control and Dynamics, ISSN 0731-5090, E-ISSN 1533-3884, Vol. 31, nr 5, s. 1497-1500Artikel i tidskrift (Refereegranskat)
  • 1867.
    Öktem, Ozan
    et al.
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).
    Chen, Chong
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.). Chinese Academy of Sciences, China.
    Onur Domaniç, N.
    Ravikumar, P.
    Bajaj, C.
    Shape-based image reconstruction using linearized deformations2017Ingår i: Inverse Problems, ISSN 0266-5611, E-ISSN 1361-6420, Vol. 33, nr 3, artikel-id 035004Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We introduce a reconstruction framework that can account for shape related prior information in imaging-related inverse problems. It is a variational scheme that uses a shape functional, whose definition is based on deformable template machinery from computational anatomy. We prove existence and, as a proof of concept, we apply the proposed shape-based reconstruction to 2D tomography with very sparse and/or highly noisy measurements.

  • 1868.
    Örtenberg, Alexander
    et al.
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Medicinska fakulteten.
    Magnusson, Maria
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Medicinska fakulteten.
    Sandborg, Michael
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US.
    Alm Carlsson, Gudrun
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Centrum för kirurgi, ortopedi och cancervård, Radiofysikavdelningen US.
    Malusek, Alexandr
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Medicinska fakulteten.
    PARALLELISATION OF THE MODEL-BASED ITERATIVE RECONSTRUCTION ALGORITHM DIRA2016Ingår i: Radiation Protection Dosimetry, ISSN 0144-8420, E-ISSN 1742-3406, Vol. 169, nr 1-4, s. 405-409Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    New paradigms for parallel programming have been devised to simplify software development on multi-core processors and many-core graphical processing units (GPU). Despite their obvious benefits, the parallelisation of existing computer programs is not an easy task. In this work, the use of the Open Multiprocessing (OpenMP) and Open Computing Language (OpenCL) frameworks is considered for the parallelisation of the model-based iterative reconstruction algorithm DIRA with the aim to significantly shorten the code’s execution time. Selected routines were parallelised using OpenMP and OpenCL libraries; some routines were converted from MATLAB to C and optimised. Parallelisation of the code with the OpenMP was easy and resulted in an overall speedup of 15 on a 16-core computer. Parallelisation with OpenCL was more difficult owing to differences between the central processing unit and GPU architectures. The resulting speedup was substantially lower than the theoretical peak performance of the GPU; the cause was explained.

  • 1869.
    Östlund, C
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för ekologi och evolution, Limnologi. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Flink, P
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för ekologi och evolution. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för ekologi och evolution, Limnologi.
    Strömbeck, N
    Pierson, D
    Lindell, T
    Mapping of the water quality of Lake Erken, Sweden, from Imaging Spectrometry and Landsat Thematic Mapper2001Ingår i: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 268, nr 1-3, s. 139-154Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Hyperspectral data have been collected by the Compact Airborne Spectrographic Imager (CASI) and multispectral data by the Landsat Thematic Mapper (TM) instrument for the purpose of mapping lake water quality. Field campaigns have been performed on Lake Erken

  • 1870.
    Östlund, Catherine
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys.
    Analysis of Imaging Spectrometer Data with Lake Environment Applications1999Doktorsavhandling, monografi (Övrigt vetenskapligt)
    Abstract [en]

    In this thesis the image processing and analysis aspects of imaging spectrometer (IS) data have been investigated for water and wetland applications. The Compact Airborne Spectrographic Imager (CASI) has been the main instrument in the evaluations. To fully benefit from the high spectral and spatial resolution data in the analysis phase, the preprocessing of data, is important and has been a focus of this thesis. To restore, improve and evaluate the data, the radiometric calibration, wavelength band positioning, noise and other radiometric anomalies, geometric calibration and atmospheric calibration have been studied. Existing methods have been evaluated, and new ones proposed, and the most appropriate methods applied to the data.

    On the image analysis aspects of hyperspectral data sets, spatial true physical structures in the images were studied using data compression and segmentation methods, and a new technique combining compression and colour transformation. The latter was shown to be a fast and objective method to visualise the spatial structures in a large data set.

    The usefulness of IS data in water quality applications was evaluated developing statistical relationships between image data and data collected in the field. A comprehensive in situ data set, collected along a transect in Lake Erken, Sweden, during a bloom of the cyanobacteria Gloeotrichia echinulata was used. It was found that a correlation of the image data to chlorophyll a and phaeophytine a could be established, but also that the preprocessing of images is important, and that the dynamic character of water is a complicating factor. Aquatic macrophytes in Lake Mälaren, Sweden, were classified. IS data was found to be powerful for these kinds of applications, but the analysis suffered from poor data.

  • 1871.
    Ćurić, Vladimir
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Distance Functions and Their Use in Adaptive Mathematical Morphology2014Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    One of the main problems in image analysis is a comparison of different shapes in images. It is often desirable to determine the extent to which one shape differs from another. This is usually a difficult task because shapes vary in size, length, contrast, texture, orientation, etc. Shapes can be described using sets of points, crisp of fuzzy. Hence, distance functions between sets have been used for comparing different shapes.

    Mathematical morphology is a non-linear theory related to the shape or morphology of features in the image, and morphological operators are defined by the interaction between an image and a small set called a structuring element. Although morphological operators have been extensively used to differentiate shapes by their size, it is not an easy task to differentiate shapes with respect to other features such as contrast or orientation. One approach for differentiation on these type of features is to use data-dependent structuring elements.

    In this thesis, we investigate the usefulness of various distance functions for: (i) shape registration and recognition; and (ii) construction of adaptive structuring elements and functions.

    We examine existing distance functions between sets, and propose a new one, called the Complement weighted sum of minimal distances, where the contribution of each point to the distance function is determined by the position of the point within the set. The usefulness of the new distance function is shown for different image registration and shape recognition problems. Furthermore, we extend the new distance function to fuzzy sets and show its applicability to classification of fuzzy objects.

    We propose two different types of adaptive structuring elements from the salience map of the edge strength: (i) the shape of a structuring element is predefined, and its size is determined from the salience map; (ii) the shape and size of a structuring element are dependent on the salience map. Using this salience map, we also define adaptive structuring functions. We also present the applicability of adaptive mathematical morphology to image regularization. The connection between adaptive mathematical morphology and Lasry-Lions regularization of non-smooth functions provides an elegant tool for image regularization.

  • 1872.
    Šarić, Marin
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Ek, Carl Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragić, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Dimensionality Reduction via Euclidean Distance Embeddings2011Rapport (Övrigt vetenskapligt)
    Abstract [en]

    This report provides a mathematically thorough review and investigation of Metric Multidimensional scaling (MDS) through the analysis of Euclidean distances in input and output spaces. By combining a geometric approach with modern linear algebra and multivariate analysis, Metric MDS is viewed as a Euclidean distance embedding transformation that converts between coordinate and coordinate-free representations of data. In this work we link Mercer kernel functions, data in infinite-dimensional Hilbert space and coordinate-free distance metrics to a finite-dimensional Euclidean representation. We further set a foundation for a principled treatment of non-linear extensions of MDS as optimization programs on kernel matrices and Euclidean distances.

35363738 1851 - 1872 av 1872
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