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  • 1.
    Forssen, Per-Erik
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Johansson, Björn
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Fractal Coding by Means of Local Feature Histograms2000Report (Other academic)
    Abstract [en]

    This report describes an experimental still image coder that grew out of a project in the graduate course ``Advanced Video Coding'' in spring 2000. The project has investigated the idea to use local orientation histograms in fractal coding. Instead of performing a correlation-like grey-level matching of image regions, the block search is made by matching feature histograms of the block contents. The feature investigated in this report is local orientation, but in principle other features could be used as well. In its current state the coder does not outperform state of the art still image coders, but the block-search strategy seems promising, and will probably prove useful in several other applications.

  • 2.
    Forssén, Per-Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Johansson, Björn
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Channel Associative Networks for Multiple Valued Mappings2006In: 2nd International Cognitive Vision Workshop, 2006, p. 4-11Conference paper (Other academic)
  • 3.
    Forssén, Per-Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Johansson, Björn
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Learning under Perceptual Aliasing2005Report (Other academic)
  • 4.
    Granlund, Gösta
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Forssén, Per-Erik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Johansson, Björn
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    HiperLearn: A High Performance Learning Architecture2002Report (Other academic)
    Abstract [en]

    A new architecture for learning systems has been developed. A number of particular design features in combination result in a very high performance and excellent robustness. The architecture uses a monopolar channel information representation. The channel representation implies a partially overlapping mapping of signals into a higher-dimensional space, such that a flexible but continuous restructuring mapping can be made. The high-dimensional mapping introduces locality in the information representation, which is directly available in wavelets or filter outputs. Single level maps using this representation can produce closed decision regions, thereby eliminating the need for costly back-propagation. The monopolar property implies that data only utilizes one polarity, say positive values, in addition to zero, allowing zero to represent no information. This leads to an efficient sparse representation.

    The processing mode of the architecture is association where the mapping of feature inputs onto desired state outputs is learned from a representative training set. The sparse monopolar representation together with locality, using individual learning rates, allows a fast optimization, as the system exhibits linear complexity. Mapping into multiple channels gives a strategy to use confidence statements in data, leading to a low sensitivity to noise in features. The result is an architecture allowing systems with a complexity of some hundred thousand features described by some hundred thousand samples to be trained in typically less than an hour. Experiments that demonstrate functionality and noise immunity are presented. The architecture has been applied to the design of hyper complex operations for view centered object recognition in robot vision.

  • 5.
    Hedborg, Johan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Johansson, Björn
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Real time camera ego-motion compensation and lens undistortion on GPU2007Manuscript (preprint) (Other academic)
    Abstract [en]

    This paper describes a GPU implementation for simultaneous camera ego-motion compensation and lens undistortion. The main idea is to transform the image under an ego-motion constraint so that trackedpoints in the image, that are assumed to come from the ego-motion, maps as close as possible to their averageposition in time. The lens undistortion is computed si-multaneously. We compare the performance with and without compensation using two measures; mean timedifference and mean statistical background subtraction.

  • 6.
    Johansson, Björn
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    A Survey on: Contents Based Search in Image Databases2000Report (Other academic)
    Abstract [en]

    This survey contains links and facts to a number of projects on content based search in image databases around the world today. The main focus is on what kind of image features is used but also the user interface and the users possibility to interact with the system (i.e. what 'visual language' is used).

  • 7.
    Johansson, Björn
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Backprojection of Some Image Symmetries Based on a Local Orientation Description2000Report (Other academic)
    Abstract [en]

    Some image patterns, e.g. circles, hyperbolic curves, star patterns etc., can be described in a compact way using local orientation. The features mentioned above is part of a family of patterns called rotational symmetries. This theory can be used to detect image patterns from the local orientation in double angle representation of an images. Some of the rotational symmetries are described originally from the local orientation without being designed to detect a certain feature. The question is then: given a description in double angle representation, what kind of image features does this description correspond to? This 'inverse', or backprojection, is not unambiguous - many patterns has the same local orientation description. This report answers this question for the case of rotational symmetries and also for some other descriptions.

  • 8.
    Johansson, Björn
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Curvature Detection using Polynomial Fitting on Local Orientation2000Report (Other academic)
    Abstract [en]

    This report describes a technique to detect curvature. The technique uses local polynomial fitting on a local orientation description of an image. The idea is based on the theory of rotational symmetries which describes curvature, circles, star-patterns etc. The local polynomial fitting is shown to be equivalent to calculating partial derivatives on a lowpass version of the local orientation. The new method can therefore be very efficiently implemented both in the singlescale case and in the multiscale case.

  • 9.
    Johansson, Björn
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Low Level Operations and Learning in Computer Vision2004Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis presents some concepts and methods for low level computer vision and learning, with object recognition as the primary application.

    An efficient method for detection of local rotational symmetries in images is presented. Rotational symmetries include circle patterns, star patterns, and certain high curvature patterns. The method for detection of these patterns is based on local moments computed on a local orientation description in double angle representation, which makes the detection invariant to the sign of the local direction vectors. Some methods are also suggested to increase the selectivity of the detection method. The symmetries can serve as feature descriptors and interest points for use in hierarchical matching structures for object recognition and related problems.

    A view-based method for 3D object recognition and estimation of object pose from a single image is also presented. The method is based on simple feature vector matching and clustering. Local orientation regions computed at interest points are used as features for matching. The regions are computed such that they are invariant to translation, rotation, and locally invariant to scale. Each match casts a vote on a certain object pose, rotation, scale, and position, and a joint estimate is found by a clustering procedure. The method is demonstrated on a number of real images and the region features are compared with the SIFT descriptor, which is another standard region feature for the same application.

    Finally, a new associative network is presented which applies the channel representation for both input and output data. This representation is sparse and monopolar, and is a simple yet powerful representation of scalars and vectors. It is especially suited for representation of several values simultaneously, a property that is inherited by the network and something which is useful in many computer vision problems. The chosen representation enables us to use a simple linear model for non-linear mappings. The linear model parameters are found by solving a least squares problem with a non-negative constraint, which gives a sparse regularized solution.

  • 10.
    Johansson, Björn
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    On Classification: Simultaneously Reducing Dimensionality and Finding Automatic Representation using Canonical Correlation2001Report (Other academic)
    Abstract [en]

    This report describes an idea based on the work in [1], where an algorithm for learning automatic representation of visual operators is presented. The algorithm in [1] uses canonical correlation to find a suitable subspace in which the signal is invariant to some desired properties. This report presents a related approach specially designed for classification problems. The goal is to find a subspace in which the signal is invariant within each class, and at the same time compute the class representation in that subspace. This algorithm is closely related to the one in [1], but less computationally demanding, and it is shown that the two algorithms are equivalent if we have equal number of training samples for each class. Even though the new algorithm is designed for pure classification problems it can still be used to learn visual operators as will be shown in the experiment section. [1] M. Borga. Learning Multidimensional Signal Processing. PhD thesis, Linköping University, Sweden, SE-581 83 Linköping, 1998. Dissertation No 531, ISBN 91-7219-202-X.

  • 11.
    Johansson, Björn
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    On Sparse Associative Networks: A Least Squares Formulation2001Report (Other academic)
    Abstract [en]

    This report is a complement to the working document [1], where a sparse associative network is described. This report shows that the net learning rule in [1] can be viewed as the solution to a weighted least squares problem. This means that we can apply the theory framework of least squares problems, and compare the net rule with some other iterative algorithms that solve the same problem. The learning rule is compared with the gradient search algorithm and the RPROP algorithm in a simple synthetic experiment. The gradient rule has the slowest convergence while the associative and the RPROP rules have similar convergence. The associative learning rule has a smaller initial error than the RPROP rule though.

    It is also shown in the same experiment that we get a faster convergence if we have a monopolar constraint on the solution, i.e. if the solution is constrained to be non-negative. The least squares error is a bit higher but the norm of the solution is smaller, which gives a smaller interpolation error.

    The report also discusses a generalization of the least squares model, which include other known function approximation models.

    [1] G Granlund. Paralell Learning in Artificial Vision Systems: Working Document. Dept. EE, Linköping University, 2000

  • 12.
    Johansson, Björn
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Representing Multiple Orientations in 2D with Orientation Channel Histograms2002Report (Other academic)
    Abstract [en]

    The channel representation is a simple yet powerful representation of scalars and vectors. It is especially suited for representation of several scalars at the same time without mixing them up.

    This report is partly intended to serve as a simple illustration of the channel representation. The report shows how the channels can be used to represent multiple orientations in two dimensions. The idea is to make a channel representation of the local orientation angle computed from the image gradient. The representation basically becomes an orientation histogram with overlapping bins.

    The channel histogram is compared with the orientation tensor, which is another representation of orientation. The performance comparable to tensors in the simple signal case, but decreases slightly for increasing number of channels. The channel histogram outperforms the tensors on non-simple signals.

  • 13.
    Johansson, Björn
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Rotational Symmetries, a Quick Tutorial2001Other (Other academic)
  • 14.
    Johansson, Björn
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Learning Corner Orientation Using Canonical Correlation2001In: Proceedings of the SSAB Symposium on Image Analysis: Norrköping, 2001, p. 89-92Conference paper (Refereed)
    Abstract [en]

    This paper shows how canonical correlation can be used to learn a detector for corner orientation invariant to corner angle and intensity. Pairs of images with the same corner orientation but different angle and intensity are used as training samples. Three different image representations; intensity values, products between intensity values, and local orientation are examined. The last representation gives a well behaved result that is easy to decode into the corner orientation. To reduce dimensionality, parameters from a polynomial model fitted on the different representations is also considered. This reduction did not affect the performance of the system.

  • 15.
    Johansson, Björn
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Elfving, Tommy
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Scientific Computing.
    Kozlov, Vladimir
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Applied Mathematics.
    Censor, Y.
    Department of Mathematics, University of Haifa, Mt. Carmel, Haifa 31905, Israel.
    Forssén, Per-Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    The application of an oblique-projected Landweber method to a model of supervised learning2006In: Mathematical and computer modelling, ISSN 0895-7177, E-ISSN 1872-9479, Vol. 43, no 7-8, p. 892-909Article in journal (Refereed)
    Abstract [en]

    This paper brings together a novel information representation model for use in signal processing and computer vision problems, with a particular algorithmic development of the Landweber iterative algorithm. The information representation model allows a representation of multiple values for a variable as well as an expression for confidence. Both properties are important for effective computation using multi-level models, where a choice between models will be implementable as part of the optimization process. It is shown that in this way the algorithm can deal with a class of high-dimensional, sparse, and constrained least-squares problems, which arise in various computer vision learning tasks, such as object recognition and object pose estimation. While the algorithm has been applied to the solution of such problems, it has so far been used heuristically. In this paper we describe the properties and some of the peculiarities of the channel representation and optimization, and put them on firm mathematical ground. We consider the optimization a convexly constrained weighted least-squares problem and propose for its solution a projected Landweber method which employs oblique projections onto the closed convex constraint set. We formulate the problem, present the algorithm and work out its convergence properties, including a rate-of-convergence result. The results are put in perspective with currently available projected Landweber methods. An application to supervised learning is described, and the method is evaluated in an experiment involving function approximation, as well as application to transient signals. © 2006 Elsevier Ltd. All rights reserved.

  • 16.
    Johansson, Björn
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Elfving, Tommy
    Linköping University, Department of Mathematics, Scientific Computing. Linköping University, The Institute of Technology.
    Kozlov, Vladimir
    Linköping University, Department of Mathematics, Applied Mathematics. Linköping University, The Institute of Technology.
    Censor, Yair
    Department of Mathematics, University of Haifa, Mt. Carmel, Haifa 31905, Israel.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    The Application of an Oblique-Projected Landweber Method to a Model of Supervised Learning2004Report (Other academic)
    Abstract [en]

    This report brings together a novel approach to some computer vision problems and a particular algorithmic development of the Landweber iterative algorithm. The algorithm solves a class of high-dimensional, sparse, and constrained least-squares problems, which arise in various computer vision learning tasks, such as object recognition and object pose estimation. The algorithm has recently been applied to these problems, but it has been used rather heuristically. In this report we describe the method and put it on firm mathematical ground. We consider a convexly constrained weighted least-squares problem and propose for its solution a projected Landweber method which employs oblique projections onto the closed convex constraint set. We formulate the problem, present the algorithm and work out its convergence properties, including a rate-of-convergence result. The results are put in perspective of currently available projected Landweber methods. The application to supervised learning is described, and the method is evaluated in a function approximation experiment.

  • 17.
    Johansson, Björn
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Farnebäck, Gunnar
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    A Theoretical Comparison of Different Orientation Tensors2002In: Proceedings SSAB02 Symposium on Image Analysis,2002, 2002, p. 69-73Conference paper (Other academic)
  • 18.
    Johansson, Björn
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Fast selective detection of rotational symmetries using normalized inhibition2000In: Proceedings of the 6th European Conference on Computer Vision, Dublin, Ireland, June 26 - July 1, Part I / [ed] David Vernon, London: Springer, 2000, Vol. 1842, p. 871-887Chapter in book (Refereed)
    Abstract [en]

    Perceptual experiments indicate that corners and curvature are very important features in the process of recognition. This paper presents a new method to efficiently detect rotational symmetries, which describe complex curvature such as corners, circles, star- and spiral patterns. The method is designed to give selective and sparse responses. It works in three steps, first extract local orientation from a gray-scale or color image, second correlate the orientation image with rotational symmetry filters and third let the filter responses inhibit each other in order to get more selective responses, The correlations can be made efficient by separating the 2D-filters into a small number of 1D-filters. These symmetries can serve as feature points at a high abstraction level for use in hierarchical matching structures for 3D-estimation, object recognition, etc.

  • 19.
    Johansson, Björn
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Detecting Rotational Symmetries using Normalized Convolution2000In: Proceedings of the 15th International Conference on Pattern Recognition,2000, IEEE , 2000, p. 496-500 vol.3Conference paper (Refereed)
    Abstract [en]

    Perceptual experiments indicate that corners and curvature are very important features in the process of recognition. This paper presents a new method to detect rotational symmetries, which describes complex curvature such as corners, circles, star, and spiral patterns. It works in two steps: 1) it extracts local orientation from a gray-scale or color image; and 2) it applies normalized convolution on the orientation image with rotational symmetry filters as basis functions. These symmetries can serve as feature points at a high abstraction level for use in hierarchical matching structures for 3D estimation, object recognition, image database retrieval, etc

  • 20.
    Johansson, Björn
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Moe, Anders
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Object Recognition in 3D Laser Radar Data using Plane triplets2005Report (Other academic)
    Abstract [en]

    This report describes a method to detect and recognize objects from 3D laser radar data. The method is based on local descriptors computed from triplets of planes that are estimated from the data set. Each descriptor that is computed on query data is compared with descriptors computed on object model data to get a hypothesis of object class and pose. An hypothesis is either verified or rejected using a similarity measure between the model data set and the query data set.

  • 21.
    Johansson, Björn
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Moe, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Patch-Duplets for Object Recognition and Pose Estimation2004In: Proceedings SSBA04 Symposium on Image Analysis,2004, 2004, p. 78-81Conference paper (Other academic)
  • 22.
    Johansson, Björn
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Moe, Anders
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Patch-Duplets for Object Recognition and Pose Estimation2003Report (Other academic)
    Abstract [en]

    This report describes a view-based method for object recognition and estimation of object pose in still images. The method is based on feature vector matching and clustering. A set of interest points, in this case star-patterns, is detected and combined into pairs. A pair of patches, centered around each point in the pair, is extracted from a local orientation image. The patch orientation and size depends on the relative positions of the points, which make them invariant to translation, rotation, and scale. Each pair of patches constitutes a feature vector. The method is demonstrated on a number of real images.

  • 23.
    Johansson, Björn
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Moe, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Patch-Duplets for Object Recognition and Pose Estimation2005In: 2nd Canadian Conference on Computer and Robot Vision,2005, 2005Conference paper (Refereed)
  • 24.
    Johansson, Björn
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Söderberg, Robert
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    A Repeatability Test for Two Orientation Based Interest Point Detectors2004Report (Other academic)
    Abstract [en]

    This report evaluates the stability of two image interest point detectors, star-pattern points and points based on the fourth order tensor. The Harris operator is also included for comparison. Different image transformations are applied and the repeatability of points between a reference image and each of the transformed images are computed. The transforms are plane rotation, change in scale, change in view, and change in lightning conditions. We conclude that the result largely depends on the image content. The star-pattern points and the fourth order tensor models the image as locally straight lines, while the Harris operator is based on simple/non-simple signals. The two methods evaluated here perform equally well or better than the Harris operator if the model is valid, and perform worse otherwise.

  • 25.
    Johansson, Björn
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Forssén, Per-Erik
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Combining shadow detection and simulation for estimation of vehicle size and position2009In: PATTERN RECOGNITION LETTERS, ISSN 0167-8655, Vol. 30, no 8, p. 751-759Article in journal (Refereed)
    Abstract [en]

    This paper presents a method that combines shadow detection and a 3D box model including shadow simulation, for estimation of size and position of vehicles. We define a similarity measure between a simulated image of a 3D box, including the box shadow, and a captured image that is classified into background/foreground/shadow. The similarity Measure is used in all optimization procedure to find the optimal box state. It is shown in a number of experiments and examples how the combination shadow detection/simulation improves the estimation compared to just using detection or simulation, especially when the shadow detection or the simulation is inaccurate. We also describe a tracking system that utilizes the estimated 3D boxes, including highlight detection, spatial window instead of a time based window for predicting heading, and refined box size estimates by weighting accumulated estimates depending oil view. Finally, we show example results.

  • 26.
    Johansson, Björn
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Goals and status within the IVSS project2006In: Seminar on "Cognitive vision in traffic analyses": Lund, Sweden, 2006Conference paper (Refereed)
  • 27. Källhammer, Jan-Erik
    et al.
    Eriksson, Dick
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Moe, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Johansson, Björn
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Wiklund, Johan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Forssén, Per-Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Near Zone Pedestrian Detection using a Low-Resolution FIR Sensor2007In: Intelligent Vehicles Symposium, 2007 IEEE, Istanbul, Turkey: IEEE , 2007, , p. 339-345Conference paper (Refereed)
    Abstract [en]

    This paper explores the possibility to use a single low-resolution FIR camera for detection of pedestrians in the near zone in front of a vehicle. A low resolution sensor reduces the cost of the system, as well as the amount of data that needs to be processed in each frame.

    We present a system that makes use of hot-spots and image positions of a near constant bearing to detect potential pedestrians. These detections provide seeds for an energy minimization algorithm that fits a pedestrian model to the detection. Since false alarms are hard to tolerate, the pedestrian model is then tracked, and the distance-to-collision (DTC) is measured by integrating size change measurements at sub-pixel accuracy, and the car velocity. The system should only engage braking for detections on a collision course, with a reliably measured DTC.

    Preliminary experiments on a number of recorded near collision sequences indicate that our method may be useful for ranges up to about 10m using an 80x60 sensor, and somewhat more using a 160x120 sensor. We also analyze the robustness of the evaluated algorithm with respect to dead pixels, a potential problem for low-resolution sensors.

  • 28.
    spies, hagen
    et al.
    Dept. of Electrical Engineering Linköping University.
    Johansson, Björn
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Directional Channel Representation for Multiple Line-Endings and Intensity Levels2003In: Proceedings of IEEE International Conference on Image Processing,2003, 2003, p. 265-268Conference paper (Refereed)
  • 29.
    Viksten, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Forssén, Per-Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Johansson, Björn
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Moe, Anders
    SICK/IVP.
    Comparison of Local Image Descriptors for Full 6 Degree-of-Freedom Pose Estimation2009In: IEEE ICRA, 2009: 1050-4729, Kobe: IEEE Robotics and Automation Society , 2009, p. 2779-2786Conference paper (Refereed)
    Abstract [en]

    Recent years have seen advances in the estimation of full 6 degree-of-freedom object pose from a single 2D image. These advances have often been presented as a result of, or together with, a new local image descriptor. This paper examines how the performance for such a system varies with choice of local descriptor. This is done by comparing the performance of a full 6 degree-of-freedom pose estimation system for fourteen types of local descriptors. The evaluation is done on a database with photos of complex objects with simple and complex backgrounds and varying lighting conditions. From the experiments we can conclude that duplet features, that use pairs of interest points, improve pose estimation accuracy, and that affine covariant features do not work well in current pose estimation frameworks. The data sets and their ground truth is available on the web to allow future comparison with novel algorithms.

  • 30.
    Viksten, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
    Forssén, Per-Erik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Johansson, Björn
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Moe, Anders
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Local Image Descriptors for Full 6 Degree-of-Freedom Object Pose Estimation and Recognition2010Article in journal (Refereed)
    Abstract [en]

    Recent years have seen advances in the estimation of full 6 degree-of-freedom object pose from a single 2D image. These advances have often been presented as a result of, or together with, a new local image feature type. This paper examines how the pose accuracy and recognition robustness for such a system varies with choice of feature type. This is done by evaluating a full 6 degree-of-freedom pose estimation system for 17 different combinations of local descriptors and detectors. The evaluation is done on data sets with photos of challenging 3D objects with simple and complex backgrounds and varying illumination conditions. We examine the performance of the system under varying levels of object occlusion and we find that many features allow considerable object occlusion. From the experiments we can conclude that duplet features, that use pairs of interest points, improve pose estimation accuracy, compared to single point features. Interestingly, we can also show that many features previously used for recognition and wide-baseline stereo are unsuitable for pose estimation, one notable example are the affine covariant features that have been proven quite successful in other applications. The data sets and their ground truths are available on the web to allow future comparison with novel algorithms.

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