Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 72 for “"Sparse representation"”.
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Learning sparse representation for image signals
… have the intrinsic property that they can be sparsely represented as a linear combination of a very small number of atomic signals from a complete basis or an overcomplete dictionary. This sparse representation prior has been successfully exploited in a variety of image processing …
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Image super-resolution via sparse representation
… to single-image super-resolution (SR), based on sparse signal recovery. Research on image statistics suggests that image patches can be well represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse …
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Sparse representation in deep vision models
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01
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An exploration into the sparse representation of spectra
This thesis describes an exploration in achieving sparse representations of object, with special focus on spectral data. Given a database of objects one would like to know the actual aspects of each class that distinguish it from any other class in the database. We explore the hypothesis that …
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Motor imagery classification using sparse representation of EEG signals
… explore and develop algorithms that exploit the sparse characteristics of EEGs to classify these signals. Different feature vectors are extracted from EEG trials recorded by electrodes placed on the scalp. In this thesis, features from a small spatial region are approximated by a sparse linear …
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Criminal data analysis based on low rank sparse representation
… segmentation which included the Low Rank Representation (LRR) and Sparse Representation (SR). The former seeks the global lowest-rank representation but restrictively assumes the independence among subspaces, whereas the latter seeks the clustering of disjoint or overlapped subspaces …
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Sparse representation for audio noise removal using zero-zone quantizers
In zero zone quantization, bins around zero are quantized to a zero value. This kind of quantization can be applied on orthogonal transforms to remove the unwanted or redundant signal. Transforms reveal structures and properties of a signal and hence careful application of a zero zone over the …
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Sparse Representation and its Application to Multivariate Time Series Classification
… signal in order to obtain meaningful outcome. Sparse representation (SR) has continued to receive great attention as one of the well-known tools in statistical theory which among others, is used to extract specific latent temporal features that can reveal salient primitive and sparsely …
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Sparse Representation and its Application to Multivariate Time Series Classification
… signal in order to obtain meaningful outcome. Sparse representation (SR) has continued to receive great attention as one of the well-known tools in statistical theory which among others, is used to extract specific latent temporal features that can reveal salient primitive and sparsely …
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Smooth Projections and Optimally Sparse Representation of Cartoon-like Cylindrical Solids
… two mathematical structures: projections and sparse representations. Projections are self-adjoint and idempotent linear operators that can be used to decompose a function space into smaller components thus making it easier to analyze. First, we extend the construction of smooth orthogonal …
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Investigation of new learning methods for visual recognition
… recognition system are discriminatory feature representation and efficient and accurate pattern classification. This dissertation therefore focuses on developing new learning methods for visual recognition. Based on the conventional sparse representation, which shows its robustness for visual …
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Face recognition under varying illumination, pose and contiguous occlusion
… with contiguous occlusion using tools from sparse representation, which has demonstrated promising results in handling illumination changes and occlusion. While such sparsity-based algorithms achieve their best performance on occlusions that are not spatially correlated (i.e. random pixel …
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Deconvolution and sparsity based image restoration
Deconvolution and sparse representation are the two key areas in image and signal processing. In this thesis the classical image restoration problem is addressed using these two modalities. Image restoration, such as deblurring, dnoising, and in-painting belongs to the class of ill-posed linear …
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Deconvolution and sparsity based image restoration
Deconvolution and sparse representation are the two key areas in image and signal processing. In this thesis the classical image restoration problem is addressed using these two modalities. Image restoration, such as deblurring, dnoising, and in-painting belongs to the class of ill-posed linear …
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Landmine classification using possibilistic K-nearest neighbors with wideband electromagnetic induction data
… metal content, size, shape, and depth. Given a sparse representation obtained using the joint orthogonal matching pursuits algorithm, particular target types consistently selected the same dictionary elements for their sparse representation. The proposed classifier distinguishes between …
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SPARSE RECOVERY BY NONCONVEX LIPSHITZIAN MAPPINGS
… processing fields. The general framework of sparse representation is now a mature concept with solid basis in relevant mathematical fields, such as probability, geometry of Banach spaces, harmonic analysis, theory of computability, and information-based complexity. Together with theoretical …
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Sparse modeling of high-dimensional data for learning and vision
Sparse representations account for most or all of the information of a signal by a linear combination of a few elementary signals called atoms, and have increasingly become recognized as providing high performance for applications as diverse as noise reduction, compression, inpainting, compressive …
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Learning sparse features and metric in signal and image processing
… studies two aspects of feature learning: representation learning and metric in feature space, from a machine learning perspective. Feature learning is a fundamental problem in computer vision and machine learning. First introduced in computational neuroscience in the context of sparse …
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Fast superresolution based on a network structure trained using sparse coding
… to superresolution using a network structure. Sparse representation of image signals forms the cornerstone of our approach and the goal is to obtain resolution enhancement of the low resolution images. I will discuss various dictionary learning methods and also a joint dictionary training …
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