13 research outputs found

    Kernel recursive least squares dictionary learning algorithm

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    An online dictionary learning algorithm for kernel sparse representation is developed in the current paper. In this framework, the input signal nonlinearly mapped into the feature space is sparsely represented based on a virtual dictionary in the same space. At any instant, the dictionary is updated in two steps. In the first step, the input signal samples are sparsely represented in the feature space, using the dictionary that has been updated based on the previous data. In the second step, the dictionary is updated. In this paper, a novel recursive dictionary update algorithm is derived, based on the recursive least squares (RLS) approach. This algorithm gradually updates the dictionary, upon receiving one or a mini-batch of training samples. An efficient implementation of the algorithm is also formulated. Experimental results over four datasets in different fields show the superior performance of the proposed algorithm in comparison with its counterparts. In particular, the classification accuracy obtained by the dictionaries trained using the proposed algorithm gradually approaches that of the dictionaries trained in batch mode. Moreover, in spite of lower computational complexity, the proposed algorithm overdoes all existing online kernel dictionary learning algorithms.acceptedVersio

    Rusmidler i Norge 2013

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    Øvrige bidragsytere: Ellen J. Amundsen, Ola Røed Bilgrei, Kristin Buvik, Marit Edland-Gryt, Odd Hordvin, Elisabeth Kvaavik, Grethe Lauritzen, Ingeborg Lund, Marianne Lund, Thomas Anton Sandøy, Janne Scheffels, Øystein Skjælaaen, Gunnar Sæbø og Tord Finne Vedøy. SIRUS' årlige rapport Rusmidler i Norge er for 2013 utvidet med mer omfattende tekst og flere tabeller enn tidligere. Innholdet er også omorganisert og delt inn i kapitlene alkohol, tobakk, avhengighetsskapende legemidler, narkotika, sniffing, doping og tjenestetilbudet. I hvert av substanskapitlene blir det gitt en beskrivelse av de aktuelle stoffene, hvordan de virker, og deres skadepotensial og aktuelle lovreguleringer. Så følger aktuelle registerdata og data fra ulike undersøkelser. Det gis også en historisk oversikt over viktige hendelser og beslutninger i tilknytning til de forskjellige substansområdene og til tjenestetilbudet

    What does your profile picture say about you? The accuracy of thin-slice personality judgments from social networking sites made at zero-acquaintance

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    The myocardium exhibits heterogeneous nature due to scarring after Myocardial Infarction (MI). In Cardiac Magnetic Resonance (CMR) imaging, Late Gadolinium (LG) contrast agent enhances the intensity of scarred area in the myocardium. In this paper, we propose a probability mapping technique using Texture and Intensity features to describe heterogeneous nature of the scarred myocardium in Cardiac Magnetic Resonance (CMR) images after Myocardial Infarction (MI). Scarred tissue and non-scarred tissue are represented with high and low probabilities, respectively. Intermediate values possibly indicate areas where the scarred and healthy tissues are interwoven. The probability map of scarred myocardium is calculated by using a probability function based on Bayes rule. Any set of features can be used in the probability function. In the present study, we demonstrate the use of two different types of features. One is based on the mean intensity of pixel and the other on underlying texture information of the scarred and non-scarred myocardium. Examples of probability maps computed using the mean intensity of pixel and the underlying texture information are presented. We hypothesize that the probability mapping of myocardium offers alternate visualization, possibly showing the details with physiological significance difficult to detect visually in the original CMR image. The probability mapping obtained from the two features provides a way to define different cardiac segments which offer a way to identify areas in the myocardium of diagnostic importance (like core and border areas in scarred myocardiu

    Assessment of sparse-based inpainting for retinal vessel removal

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    [EN] Some important eye diseases, like macular degeneration or diabetic retinopathy, can induce changes visible on the retina, for example as lesions. Segmentation of lesions or extraction of textural features from the fundus images are possible steps towards automatic detection of such diseases which could facilitate screening as well as provide support for clinicians. For the task of detecting significant features, retinal blood vessels are considered as being interference on the retinal images. If these blood vessel structures could be suppressed, it might lead to a more accurate segmentation of retinal lesions as well as a better extraction of textural features to be used for pathology detection. This work proposes the use of sparse representations and dictionary learning techniques for retinal vessel inpainting. The performance of the algorithm is tested for greyscale and RGB images from the DRIVE and STARE public databases, employing different neighbourhoods and sparseness factors. Moreover, a comparison with the most common inpainting family, diffusion-based methods, is carried out. For this purpose, two different ways of assessing the quality of the inpainting are presented and used to evaluate the results of the non-artificial inpainting, i.e. where a reference image does not exist. The results suggest that the use of sparse-based inpainting performs very well for retinal blood vessels removal which will be useful for the future detection and classification of eye diseases. (C) 2017 Elsevier B.V. All rights reserved.This work was supported by NILS Science and Sustainability Programme (014-ABEL-IM-2013) and by the Ministerio de Economia y Competitividad of Spain, Project ACRIMA (TIN2013-46751-R). The work of Adrian Colomer has been supported by the Spanish Government under the FPI Grant BES-2014-067889.Colomer, A.; Naranjo Ornedo, V.; Engan, K.; Skretting, K. (2017). Assessment of sparse-based inpainting for retinal vessel removal. Signal Processing: Image Communication. 59:73-82. https://doi.org/10.1016/j.image.2017.03.018S73825

    Texture classification using sparse frame based representations

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    Konferanse fra NORSIG 2002, Tromsø / Trondheim, Norway, Oct. 4-7, 2002In this paper a new method for texture classification, denoted Frame Texture Classification Method (FTCM), is presented. The main idea is that a frame trained to make a sparse representation of a certain class of signals is a model for this signal class. The signal class is given by many representative image blocks of the class. Frames are trained for several textures, one frame for each texture class. A pixel of an image is classified by processing a block around the pixel, the block size is the same as the one used in the training set. Many sparse representations of this test block are found, using each of the frames trained for the texture classes under consideration. Since the frames were trained to minimize the representation error, the tested pixel is assumed to belong to the texture for which the corresponding frame has the smallest representation error. The FTCM is applied to nine test images, yielding excellent overall performance, for many test images the number of wrongly classified pixels is more than halved, in comparison to state of the art texture classification methods presented in [1]

    Partial search vector selection for sparse signal representation

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    konferanse fra NORSIG 2003, Bergen, Norway, Oct. 2-4, 2003In this paper a new algorithm for vector selection in signal representation problems is proposed, we call it space is searched. Partial Search (PS). The vector selection problem is described, and one group of algorithms for solving this problem, the Matching Pursuit (MP) algorithms, is reviewed. The proposed algorithm is based on the Order Recursive Matching Pursuit (ORMP) algorithm,it extends ORMP by searching a larger part of the solution space in an e®ective way. The PS algorithm tests up to a given number of possible solutions and returns the best, while ORMP is a greedy algorithm testing and returning only one possible solution. A detailed description of PS is given. In the end some examples of its performance are given, showing that PS performs very well, that the representation error is considerable reduced and that the probability of finding the optimal solution is increased compared to ORMP, even when only a small part of the solution space is searched

    Sparse Signal Representation using Overlapping Frames

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    Signal expansions using frames may be considered as generalizations of signal representations based on transforms and filter banks. Frames for sparse signal representations may be designed using an iterative method with two main steps: (1) Frame vector selection and expansion coefficient determination for signals in a training set, – selected to be representative of the signals for which compact representations are desired, using the frame designed in the previous iteration. (2) Update of frame vectors with the objective of improving the representation of step (1). In this thesis we solve step (2) of the general frame design problem using the compact notation of linear algebra. This makes the solution both conceptually and computationally easy, especially for the non-block-oriented frames, – for short overlapping frames, that may be viewed as generalizations of critically sampled filter banks. Also, the solution is more general than those presented earlier, facilitating the imposition of constraints, such as symmetry, on the designed frame vectors. We also take a closer look at step (1) in the design method. Some of the available vector selection algorithms are reviewed, and adaptations to some of these are given. These adaptations make the algorithms better suited for both the frame design method and the sparse representation of signals problem, both for block-oriented and overlapping frames. The performances of the improved frame design method are shown in extensive experiments. The sparse representation capabilities are illustrated both for one-dimensional and two-dimensional signals, and in both cases the new possibilities in frame design give better results. Also a new method for texture classification, denoted Frame Texture Classification Method (FTCM), is presented. The main idea is that a frame trained for making sparse representations of a certain class of signals is a model for this signal class. The FTCM is applied to nine test images, yielding excellent overall performance, for many test images the number of wrongly classified pixels is more than halved, in comparison to state of the art texture classification methods presented in [59]. Finally, frames are analyzed from a practical viewpoint, rather than in a mathematical theoretic perspective. As a result of this, some new frame properties are suggested. So far, the new insight this has given has been moderate, but we think that this approach may be useful in frame analysis in the future

    Rusmidler i Norge 2013

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    -Øvrige bidragsytere: Ellen J. Amundsen, Ola Røed Bilgrei, Kristin Buvik, Marit Edland-Gryt, Odd Hordvin, Elisabeth Kvaavik, Grethe Lauritzen, Ingeborg Lund, Marianne Lund, Thomas Anton Sandøy, Janne Scheffels, Øystein Skjælaaen, Gunnar Sæbø og Tord Finne Vedøy. SIRUS' årlige rapport Rusmidler i Norge er for 2013 utvidet med mer omfattende tekst og flere tabeller enn tidligere. Innholdet er også omorganisert og delt inn i kapitlene alkohol, tobakk, avhengighetsskapende legemidler, narkotika, sniffing, doping og tjenestetilbudet. I hvert av substanskapitlene blir det gitt en beskrivelse av de aktuelle stoffene, hvordan de virker, og deres skadepotensial og aktuelle lovreguleringer. Så følger aktuelle registerdata og data fra ulike undersøkelser. Det gis også en historisk oversikt over viktige hendelser og beslutninger i tilknytning til de forskjellige substansområdene og til tjenestetilbudet
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