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  • 1.
    Bradler, Henry
    et al.
    Goethe University of Frankfurt, Germany.
    Anne Wiegand, Birthe
    Goethe University of Frankfurt, Germany.
    Mester, Rudolf
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Goethe University of Frankfurt, Germany.
    The Statistics of Driving Sequences - and what we can learn from them2015Inngår i: 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOP (ICCVW), IEEE , 2015, s. 106-114Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The motion of a driving car is highly constrained and we claim that powerful predictors can be built that learn the typical egomotion statistics, and support the typical tasks of feature matching, tracking, and egomotion estimation. We analyze the statistics of the ground truth data given in the KITTI odometry benchmark sequences and confirm that a coordinated turn motion model, overlaid by moderate vibrations, is a very realistic model. We develop a predictor that is able to significantly reduce the uncertainty about the relative motion when a new image frame comes in. Such predictors can be used to steer the matching process from frame n to frame n + 1. We show that they can also be employed to detect outliers in the temporal sequence of egomotion parameters.

  • 2.
    Conrad, Christian
    et al.
    Goethe University, Frankfurt, Germany.
    Mertz, Matthias
    Goethe University, Frankfurt, Germany.
    Mester, Rudolf
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Contour-relaxed Superpixels2013Inngår i: / [ed] Heyden, A., Kahl, F., Olsson, C., Oskarsson, M., Tai, X.-C., Springer Berlin/Heidelberg, 2013, s. 280-293Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We propose and evaluate a versatile scheme for image pre-segmentation that generates a partition of the image into a selectable number of patches (’superpixels’), under the constraint of obtaining maximum homogeneity of the ’texture’ inside of each patch, and maximum accordance of the contours with both the image content as well as a Gibbs-Markov random field model. In contrast to current state-of-the art approaches to superpixel segmentation, ’homogeneity’ does not limit itself to smooth region-internal signals and high feature value similarity between neighboring pixels, but is applicable also to highly textured scenes. The energy functional that is to be maximized for this purpose has only a very small number of design parameters, depending on the particular statistical model used for the images.

    The capability of the resulting partitions to deform according to the image content can be controlled by a single parameter. We show by means of an extensive comparative experimental evaluation that the compactness-controlled contour-relaxed superpixels method outperforms the state-of-the art superpixel algorithms with respect to boundary recall and undersegmentation error while being faster or on a par with respect to runtime.

  • 3.
    Dederscheck, David
    et al.
    Goethe Universität Frankfurt, Visual Sensorics and Information Processing Lab, Frankfurt, Germany.
    Muller, T.
    Daimler AG, Sindelfingen, Germany.
    Mester, Rudolf
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Illumination invariance for driving scene optical flow using comparagram preselection2012Inngår i: IEEE Intelligent Vehicles Symposium (IV), Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2012, s. 742-747Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    In the recent years, advanced video sensors have become common in driver assistance, coping with the highly dynamic lighting conditions by nonlinear exposure adjustments. However, many computer vision algorithms are still highly sensitive to the resulting sudden brightness changes. We present a method that is able to estimate the relative intensity transfer function (RITF) between images in a sequence even for moving cameras. The according compensation of the input images can improve the performance of further vision tasks significantly, here demonstrated by results from optical flow. Our method identifies corresponding intensity values from areas in the images where no apparent motion is present. The RITF is then estimated from that data and regularized based on its curvature. Finally, built-in tests reliably flag image pairs with adverse conditions where no compensation could be performed. © 2012 IEEE.

  • 4.
    Eisenbach, Jens
    et al.
    Goethe University, Frankfurt, Germany.
    Mertz, Matthias
    Goethe University, Frankfurt, Germany.
    Conrad, Christian
    Goethe University, Frankfurt, Germany.
    Mester, Rudolf
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Reducing Camera Vibrations and Photometric Changes in Surveillance Video2013Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We analyze the consequences of instabilities and fluctuations, such as camera shaking and illumination/exposure changes, on typical surveillance video material and devise a systematic way to compensate these changes as much as possible. The phase correlation method plays a decisive role in the proposed scheme, since it is inherently insensitive to gain and offset changes, as well as insensitive against different linear degradations (due to time-variant motion blur) in subsequent images. We show that the listed variations can be compensated effectively, and the image data can be equilibrated significantly before a temporal change detection and/or a background-based detection is performed. We verify the usefulness of the method by comparative tests with and without stabilization, using the changedetection.net benchmark and several state-of-the-art detections methods.

  • 5.
    Fanani, Nolang
    et al.
    Goethe University of Frankfurt, Germany.
    Barnada, Marc
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Goethe University of Frankfurt, Germany.
    Mester, Rudolf
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Goethe University of Frankfurt, Germany.
    Motion Priors Estimation for Robust Matching Initialization in Automotive Applications2015Inngår i: Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I, SPRINGER INT PUBLISHING AG , 2015, Vol. 9474, s. 115-126Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Tracking keypoints through a video sequence is a crucial first step in the processing chain of many visual SLAM approaches. This paper presents a robust initialization method to provide the initial match for a keypoint tracker, from the 1st frame where a keypoint is detected to the 2nd frame, that is: when no depth information is available. We deal explicitly with the case of long displacements. The starting position is obtained through an optimization that employs a distribution of motion priors based on pyramidal phase correlation, and epipolar geometry constraints. Experiments on the KITTI dataset demonstrate the significant impact of applying a motion prior to the matching. We provide detailed comparisons to the state-of-the-art methods.

  • 6.
    Koschorrek, Philipp
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Piccini, Tommaso
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Öberg, Per
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska högskolan.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Nielsen, Lars
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska högskolan.
    Mester, Rudolf
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan. University of Frankfurt, Germany.
    A multi-sensor traffic scene dataset with omnidirectional video2013Inngår i: 2013 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), IEEE conference proceedings, 2013, s. 727-734Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The development of vehicles that perceive their environment, in particular those using computer vision, indispensably requires large databases of sensor recordings obtained from real cars driven in realistic traffic situations. These datasets should be time shaped for enabling synchronization of sensor data from different sources. Furthermore, full surround environment perception requires high frame rates of synchronized omnidirectional video data to prevent information loss at any speeds.

    This paper describes an experimental setup and software environment for recording such synchronized multi-sensor data streams and storing them in a new open source format. The dataset consists of sequences recorded in various environments from a car equipped with an omnidirectional multi-camera, height sensors, an IMU, a velocity sensor, and a GPS. The software environment for reading these data sets will be provided to the public, together with a collection of long multi-sensor and multi-camera data streams stored in the developed format.

  • 7.
    Mester, Rudolf
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Goethe University of Frankfurt, Germany.
    Motion Estimation Revisited: an Estimation-Theoretic Approach2014Inngår i: 2014 IEEE SOUTHWEST SYMPOSIUM ON IMAGE ANALYSIS AND INTERPRETATION (SSIAI 2014), IEEE , 2014, s. 113-116Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The present paper analyzes some previously unexplored aspects of motion estimation that are fundamental both for discrete block matching as well as for differential optical flow approaches a la Lucas-Kanade. It aims at providing a complete estimation-theoretic approach that makes the assumptions about noisy observations of samples from a continuous signal of a certain class explicit. It turns out that motion estimation is a combination of simultaneously estimating the true underlying continuous signal and optimizing the displacement between two hypothetical copies of this unknown signal. Practical schemes such as the current variants of Lucas-Kanade are just approximations to the fundamental estimation problem identified in the present paper. Derivatives appear as derivatives to the continuous signal representation kernels, not as ad hoc discrete derivative masks. The formulation via an explicit signal space defined by kernels is a precondition for analyzing e.g. the convergence range of iterative displacement estimation procedures, and for systematically chosing preconditioning filters. The paper sets the stage for further in-depth analysis of some fundamental issues that have so far been overlooked or ignored in motion analysis.

  • 8.
    Mester, Rudolf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Conrad, Christian
    Goethe University, Frankfurt, Germany.
    Learning Multi-View Correspondences via Subspace-Based Temporal Coincidences2013Inngår i: Proceeding Scandinavian Conference on Image Analysis, 2013, Springer Berlin/Heidelberg, 2013, s. 456-467Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    In this work we present an approach to automatically learn pixel correspondences between pairs of cameras. We build on the method of Temporal Coincidence Analysis (TCA) and extend it from the pure temporal (i.e. single-pixel) to the spatiotemporal domain. Our approach is based on learning a statistical model for local spatiotemporal image patches, determining rare, and expressive events from this model, and matching these events across multiple views. Accumulating multi-image coincidences of such events over time allows to learn the desired geometric and photometric relations. The presented method also works for strongly different viewpoints and camera settings, including substantial rotation, and translation. The only assumption that is made is that the relative orientation of pairs of cameras may be arbitrary, but fixed, and that the observed scene shows visual activity. We show that the proposed method outperforms the single pixel approach to TCA both in terms of learning speed and accuracy.

  • 9.
    Mester, Rudolf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Goethe University of Frankfurt, Germany.
    Conrad, Christian
    Goethe University of Frankfurt, Germany.
    When patches match - a statistical view on matching under illumination variation2014Inngår i: 2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), IEEE COMPUTER SOC , 2014, s. 4364-4369Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We discuss matching measures (scores and residuals) for comparing image patches under unknown affine photometric (=intensity) transformations. In contrast to existing methods, we derive a fully symmetric matching measure which reflects the fact that both copies of the signal are affected by measurement errors (noise), not only one. As it turns out, this evolves into an eigensystem problem; however a simple direct solution for all entities of interest can be given. We strongly advocate for constraining the estimated gain ratio and the estimated mean value offset to realistic ranges, thus preventing the matching scheme from locking into unrealistic correspondences.

  • 10.
    Mester, Rudolf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Univeristy of Frankfurt, Germany.
    Felsberg, MichaelLinköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för systemteknik, Datorseende.
    Pattern Recognition: 33rd DAGM Symposium, Frankfurt/Main, Germany, August 31 - September 2, 2011, Proceedings2011Konferanseproceedings (Annet vitenskapelig)
    Abstract [en]

    This book constitutes the refereed proceedings of the 33rd Symposium of the German Association for Pattern Recognition, DAGM 2011, held in Frankfurt/Main, Germany, in August/September 2011. The 20 revised full papers and 22 revised poster papers were carefully reviewed and selected from 98 submissions. The papers are organized in topical sections on object recognition, adverse vision conditions challenge, shape and matching, segmentation and early vision, robot vision, machine learning, and motion. The volume also includes the young researcher's forum, a section where a carefully jury-selected ensemble of young researchers present their Master thesis work.

  • 11.
    Ochs, Matthias
    et al.
    Goethe University of Frankfurt, Germany.
    Bradler, Henry
    Goethe University of Frankfurt, Germany.
    Mester, Rudolf
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Goethe University of Frankfurt, Germany.
    Enhanced Phase Correlation for Reliable and Robust Estimation of Multiple Motion Distributions2016Inngår i: IMAGE AND VIDEO TECHNOLOGY, PSIVT 2015, Springer Publishing Company, 2016, Vol. 9431, s. 368-379Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Phase correlation is one of the classic methods for sparse motion or displacement estimation. It is renowned in the literature for high precision and insensitivity against illumination variations. We propose several important enhancements to the phase correlation (PhC) method which render it more robust against those situations where a motion measurement is not possible (low structure, too much noise, too different image content in the corresponding measurement windows). This allows the method to perform self-diagnosis in adverse situations. Furthermore, we extend the PhC method by a robust scheme for detecting and classifying the presence of multiple motions and estimating their uncertainties. Experimental results on the Middlebury Stereo Dataset and on the KITTI Optical Flow Dataset show the potential offered by the enhanced method in contrast to the PhC implementation of OpenCV.

  • 12.
    Persson, Mikael
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Piccini, Tommaso
    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.
    Mester, Rudolf
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Frankfurt University, Germany.
    Robust Stereo Visual Odometry from Monocular Techniques2015Inngår i: 2015 IEEE Intelligent Vehicles Symposium (IV), Institute of Electrical and Electronics Engineers (IEEE), 2015, s. 686-691Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Visual odometry is one of the most active topics in computer vision. The automotive industry is particularly interested in this field due to the appeal of achieving a high degree of accuracy with inexpensive sensors such as cameras. The best results on this task are currently achieved by systems based on a calibrated stereo camera rig, whereas monocular systems are generally lagging behind in terms of performance. We hypothesise that this is due to stereo visual odometry being an inherently easier problem, rather than than due to higher quality of the state of the art stereo based algorithms. Under this hypothesis, techniques developed for monocular visual odometry systems would be, in general, more refined and robust since they have to deal with an intrinsically more difficult problem. In this work we present a novel stereo visual odometry system for automotive applications based on advanced monocular techniques. We show that the generalization of these techniques to the stereo case result in a significant improvement of the robustness and accuracy of stereo based visual odometry. We support our claims by the system results on the well known KITTI benchmark, achieving the top rank for visual only systems∗ .

  • 13.
    Piccini, Tommaso
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Persson, Mikael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Nordberg, Klas
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Mester, Rudolf
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. VSI, Frankfurt University.
    Good Edgels to Track: Beating the Aperture Problem with Epipolar Geometry2015Inngår i: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II / [ed] Agapito, Lourdes and Bronstein, Michael M. and Rother, Carsten, Elsevier, 2015, s. 652-664Konferansepaper (Fagfellevurdert)
    Abstract [en]

    An open issue in multiple view geometry and structure from motion, applied to real life scenarios, is the sparsity of the matched key-points and of the reconstructed point cloud. We present an approach that can significantly improve the density of measured displacement vectors in a sparse matching or tracking setting, exploiting the partial information of the motion field provided by linear oriented image patches (edgels). Our approach assumes that the epipolar geometry of an image pair already has been computed, either in an earlier feature-based matching step, or by a robustified differential tracker. We exploit key-points of a lower order, edgels, which cannot provide a unique 2D matching, but can be employed if a constraint on the motion is already given. We present a method to extract edgels, which can be effectively tracked given a known camera motion scenario, and show how a constrained version of the Lucas-Kanade tracking procedure can efficiently exploit epipolar geometry to reduce the classical KLT optimization to a 1D search problem. The potential of the proposed methods is shown by experiments performed on real driving sequences.

  • 14.
    Pinggera, Peter
    et al.
    Daimler RandD, Germany; Goethe University of Frankfurt, Germany.
    Franke, Uwe
    Daimler RandD, Germany.
    Mester, Rudolf
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Goethe University of Frankfurt, Germany.
    High-Performance Long Range Obstacle Detection Using Stereo Vision2015Inngår i: 2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), IEEE , 2015, s. 1308-1313Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Reliable detection of obstacles at long range is crucial for the timely response to hazards by fast-moving safety-critical platforms like autonomous cars. We present a novel method for the joint detection and localization of distant obstacles using a stereo vision system on a moving platform. The approach is applicable to both static and moving obstacles and pushes the limits of detection performance as well as localization accuracy. The proposed detection algorithm is based on sound statistical tests using local geometric criteria which implicitly consider non-flat ground surfaces. To achieve maximum performance, it operates directly on image data instead of precomputed stereo disparity maps. A careful experimental evaluation on several datasets shows excellent detection performance and localization accuracy up to very large distances, even for small obstacles. We demonstrate a parallel implementation of the proposed system on a GPU that executes at real-time speeds.

  • 15.
    Zografos, Vasileios
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Ellis, Liam
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Mester, Rudolf
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Discriminative Subspace Clustering2013Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a novel method for clustering data drawn from a union of arbitrary dimensional subspaces, called Discriminative Subspace Clustering (DiSC). DiSC solves the subspace clustering problem by using a quadratic classifier trained from unlabeled data (clustering by classification). We generate labels by exploiting the locality of points from the same subspace and a basic affinity criterion. A number of classifiers are then diversely trained from different partitions of the data, and their results are combined together in an ensemble, in order to obtain the final clustering result. We have tested our method with 4 challenging datasets and compared against 8 state-of-the-art methods from literature. Our results show that DiSC is a very strong performer in both accuracy and robustness, and also of low computational complexity.

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