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In this paper, A novel classification approach based on sparse representation framework is proposed. The method finds the minimum Euclidian distance between an input patch (pattern) and atoms (templates) of a learnt-base dictionary for different classes to perform the classification task. A mathematical approach is developed to map the sparse representation vector to Euclidian distances. We show that...
Detecting pedestrians with disability in surveillance videos is practical for the implementation of automated alert/assistance technology. This paper presents a novel approach for the dimensionality reduction which employs sparse representation to improve the generalization capability of a classifier. To characterize pedestrian with disability, we create directional maps by determining the dominant...
This paper proposes a sparse representation based approach for low bit-rate image compression using the learnt over-complete dictionary of texture patches. We first propose to compress each patch of the image with sparse and compressible linear combinations (via nonzero coefficients) of texture patterns encoded in a dictionary for image patches. Then, we find out that the compressibility and sparsity...
We present a novel learning-based method for single image super-resolution (SR). Given a single input low-resolution (LR) image (and its image pyramid), we propose to learn context-specific image sparse representation, which aims at modeling the relationship between low and high-resolution image patch pairs of different context categories in terms of the learned dictionaries. To predict the SR image,...
Data sets are often modeled as samples from some probability distribution lying in a very high dimensional space. In practice, they tend to exhibit low intrinsic dimensionality, which enables both fast construction of efficient data representations and solving statistical tasks such as regression of functions on the data, or even estimation of the probability distribution from which the data is generated...
Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a common problem in block-based image/video compression, especially at low bitrate coding. Various post-processing techniques have been proposed to reduce blocking artifacts, but they usually introduce excessive blurring or ringing effects. This paper proposes a self-learning-based image/ video...
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