Paper Title: Sparse Representation for Computer Vision and Pattern Recognition for Object detection

Author's name: Anvar Shathik J, Veerabhadra Babu D

Researchers in the domains of signal processing, image processing, computer vision, and pattern recognition have given sparse representation a lot of attention. In both theoretical research and real-world applications, sparse representation has a solid reputation. For sparse representation, numerous different algorithms have been put forth. There are many perspectives from which the taxonomy of sparse representation approaches can be investigated. For instance, the methods can be roughly divided into five groups based on the various norm minimizations used in sparsity constraints: sparse representation with lo-norm minimization, sparse representation with lp-norm minimization, sparse representation with l1-norm minimization, sparse representation with l2;1-norm minimization, and sparse representation with l2-norm minimization. Techniques from sparse signal representation are starting to have a considerable impact in computer vision, frequently on non-traditional applications where the objective is to extract semantic information as well as a compact, high-fidelity representation of the observed signal. In order to close this gap, the dictionary used is crucial. Unconventional dictionaries made from, or learned from, training samples themselves hold the key to achieving cutting-edge outcomes and giving semantic meaning to sparse signal representations. It also necessitates the development of new algorithmic and analytical tools to comprehend the superior performance of such unusual dictionaries.

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