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  • 1401.
    Åström, Freddie
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Variational Tensor-Based Models for Image Diffusion in Non-Linear Domains2015Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This dissertation addresses the problem of adaptive image filtering.

    Although the topic has a long history in the image processing community, researchers continuously present novel methods to obtain ever better image restoration results.

    With an expanding market for individuals who wish to share their everyday life on social media, imaging techniques such as compact cameras and smart phones are important factors. Naturally, every producer of imaging equipment desires to exploit cheap camera components while supplying high quality images. One step in this pipeline is to use sophisticated imaging software including, e.g., noise reduction to reduce manufacturing costs, while maintaining image quality.

    This thesis is based on traditional formulations such as isotropic and tensor-based anisotropic diffusion for image denoising. The difference from main-stream denoising methods is that this thesis explores the effects of introducing contextual information as prior knowledge for image denoising into the filtering schemes. To achieve this, the adaptive filtering theory is formulated from an energy minimization standpoint. The core contributions of this work is the introduction of a novel tensor-based functional which unifies and generalises standard diffusion methods. Additionally, the explicit Euler-Lagrange equation is derived which, if solved, yield the stationary point for the minimization problem. Several aspects of the functional are presented in detail which include, but are not limited to, tensor symmetry constraints and convexity. Also, the classical problem of finding a variational formulation to a given tensor-based partial differential equation is studied.

    The presented framework is applied in problem formulation that includes non-linear domain transformation, e.g., visualization of medical images.

    Additionally, the framework is also used to exploit locally estimated probability density functions or the channel representation to drive the filtering process.

    Furthermore, one of the first truly tensor-based formulations of total variation is presented. The key to the formulation is the gradient energy tensor, which does not require spatial regularization of its tensor components. It is shown empirically in several computer vision applications, such as corner detection and optical flow, that the gradient energy tensor is a viable replacement for the commonly used structure tensor. Moreover, the gradient energy tensor is used in the traditional tensor-based anisotropic diffusion scheme. This approach results in significant improvements in computational speed when the scheme is implemented on a graphical processing unit compared to using the commonly used structure tensor.

  • 1402.
    Åström, Freddie
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Baravdish, George
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    A Tensor Variational Formulation of Gradient Energy Total Variation2015In: ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, EMMCVPR 2015, Springer Berlin/Heidelberg, 2015, Vol. 8932, p. 307-320Conference paper (Refereed)
    Abstract [en]

    We present a novel variational approach to a tensor-based total variation formulation which is called gradient energy total variation, GETV. We introduce the gradient energy tensor into the GETV and show that the corresponding Euler-Lagrange (E-L) equation is a tensor-based partial differential equation of total variation type. Furthermore, we give a proof which shows that GETV is a convex functional. This approach, in contrast to the commonly used structure tensor, enables a formal derivation of the corresponding E-L equation. Experimental results suggest that GETV compares favourably to other state of the art variational denoising methods such as extended anisotropic diffusion (EAD) and total variation (TV) for gray-scale and colour images.

  • 1403.
    Åström, Freddie
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.
    On the Choice of Tensor Estimation for Corner Detection, Optical Flow and Denoising2015In: COMPUTER VISION - ACCV 2014 WORKSHOPS, PT II / [ed] C.V. Jawahar and Shiguang Shan, Springer, 2015, Vol. 9009, p. 15p. 16-30Conference paper (Refereed)
    Abstract [en]

    Many image processing methods such as corner detection,optical flow and iterative enhancement make use of image tensors. Generally, these tensors are estimated using the structure tensor. In this work we show that the gradient energy tensor can be used as an alternativeto the structure tensor in several cases. We apply the gradient energy tensor to common image problem applications such as corner detection, optical flow and image enhancement. Our experimental results suggest that the gradient energy tensor enables real-time tensor-based image enhancement using the graphical processing unit (GPU) and we obtain 40% increase of frame rate without loss of image quality.

  • 1404.
    Åström, Freddie
    et al.
    Heidelberg University, Germany.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Baravdish, George
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Mapping-Based Image Diffusion2017In: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, Vol. 57, no 3, p. 293-323Article in journal (Refereed)
    Abstract [en]

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

  • 1405.
    Åström, Freddie
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Baravdish, George
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Targeted Iterative Filtering2013Conference paper (Refereed)
    Abstract [en]

    The assessment of image denoising results depends on the respective application area, i.e. image compression, still-image acquisition, and medical images require entirely different behavior of the applied denoising method. In this paper we propose a novel, nonlinear diffusion scheme that is derived from a linear diffusion process in a value space determined by the application. We show that application-driven linear diffusion in the transformed space compares favorably with existing nonlinear diffusion techniques. 

  • 1406.
    Åström, Freddie
    et al.
    Heidelberg Collaboratory for Image Processing Heidelberg University Heidelberg, Germany.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Scharr, Hanno
    BG-2: Plant Sciences Forschungszentrum Jülich 52425, Jülich, Germany.
    Adaptive sharpening of multimodal distributions2015In: Colour and Visual Computing Symposium (CVCS), 2015 / [ed] Marius Pedersen and Jean-Baptiste Thomas, IEEE , 2015Conference paper (Refereed)
    Abstract [en]

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

  • 1407.
    Åström, Freddie
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Zografos, Vasileios
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Density Driven Diffusion2013In: 18th Scandinavian Conferences on Image Analysis, 2013, 2013, p. 718-730Conference paper (Refereed)
    Abstract [en]

    In this work we derive a novel density driven diffusion scheme for image enhancement. Our approach, called D3, is a semi-local method that uses an initial structure-preserving oversegmentation step of the input image.  Because of this, each segment will approximately conform to a homogeneous region in the image, allowing us to easily estimate parameters of the underlying stochastic process thus achieving adaptive non-linear filtering. Our method is capable of producing competitive results when compared to state-of-the-art methods such as non-local means, BM3D and tensor driven diffusion on both color and grayscale images.

  • 1408.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Combining Vision, Machine Learning and Automatic Control to Play the Labyrinth Game2012In: Proceedings of SSBA, Swedish Symposium on Image Analysis, 2012, 2012Conference paper (Other academic)
    Abstract [en]

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

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

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

  • 1409.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Integrating Learning and Optimization for Active Vision Inverse Kinematics2013In: Proceedings of SSBA, Swedish Symposium on Image Analysis, 2013, 2013Conference paper (Other academic)
  • 1410.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Online Learning and Mode Switching for Autonomous Driving from Demonstration2014In: Proceedings of SSBA, Swedish Symposium on Image Analysis, 2014, 2014Conference paper (Other academic)
  • 1411.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Weighted Update and Comparison for Channel-Based Distribution Field Tracking2015In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II, Springer, 2015, Vol. 8926, p. 218-231Conference paper (Refereed)
    Abstract [en]

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

  • 1412.
    Öfjäll, Kristoffer
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Robinson, Andreas
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Visual Autonomous Road Following by Symbiotic Online Learning2016In: Intelligent Vehicles Symposium (IV), 2016 IEEE, 2016, p. 136-143Conference paper (Refereed)
    Abstract [en]

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

  • 1413.
    Örtenberg, Alexander
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences.
    Magnusson, Maria
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering. Linköping University, Faculty of Medicine and Health Sciences.
    Sandborg, Michael
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Alm Carlsson, Gudrun
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Malusek, Alexandr
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    PARALLELISATION OF THE MODEL-BASED ITERATIVE RECONSTRUCTION ALGORITHM DIRA2016In: Radiation Protection Dosimetry, ISSN 0144-8420, E-ISSN 1742-3406, Vol. 169, no 1-4, p. 405-409Article in journal (Refereed)
    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.

  • 1414.
    Özarslan, Evren
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Yolcu, Cem
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Herberthson, Magnus
    Linköping University, Department of Mathematics, Mathematics and Applied Mathematics. Linköping University, Faculty of Science & Engineering.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Westin, Carl-Fredrik
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Laboratory for Mathematics in Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
    Influence of the Size and Curvedness of Neural Projections on the Orientationally Averaged Diffusion MR Signal2018In: Frontiers in Physics, E-ISSN 2296-424X, Vol. 6, p. 1-10, article id 17Article in journal (Refereed)
    Abstract [en]

    Neuronal and glial projections can be envisioned to be tubes of infinitesimal diameter as far as diffusion magnetic resonance (MR) measurements via clinical scanners are concerned. Recent experimental studies indicate that the decay of the orientationally-averaged signal in white-matter may be characterized by the power-law, Ē(q) ∝ q−1, where q is the wavenumber determined by the parameters of the pulsed field gradient measurements. One particular study by McKinnon et al. [1] reports a distinctively faster decay in gray-matter. Here, we assess the role of the size and curvature of the neurites and glial arborizations in these experimental findings. To this end, we studied the signal decay for diffusion along general curves at all three temporal regimes of the traditional pulsed field gradient measurements. We show that for curvy projections, employment of longer pulse durations leads to a disappearance of the q−1 decay, while such decay is robust when narrow gradient pulses are used. Thus, in clinical acquisitions, the lack of such a decay for a fibrous specimen can be seen as indicative of fibers that are curved. We note that the above discussion is valid for an intermediate range of q-values as the true asymptotic behavior of the signal decay is Ē(q) ∝ q−4 for narrow pulses (through Debye-Porod law) or steeper for longer pulses. This study is expected to provide insights for interpreting the diffusion-weighted images of the central nervous system and aid in the design of acquisition strategies.

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