Sparsity constrained estimation in image processing and computer vision

Vishal Monga, Hojjat Seyed Mousavi, Umamahesh Srinivas

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    Over the past decade, sparsity has emerged as a dominant theme in signal processing and big data applications. In this chapter, we formulate and solve new flavors of sparsity-constrained optimization problems built on the family of spike-and-slab priors. First, we develop an efficient Iterative Convex Refinement solution to the hard non-convex problem of Bayesian signal recovery under sparsity-inducing spike-and-slab priors. We also offer a Bayesian perspective on sparse representation-based classification via the introduction of class-specific priors. This formulation represents a consummation of ideas developed for model-based compressive sensing into a general framework for sparse model-based classification.

    Original languageEnglish (US)
    Title of host publicationHandbook of Convex Optimization Methods in Imaging Science
    PublisherSpringer International Publishing
    Pages177-206
    Number of pages30
    ISBN (Electronic)9783319616094
    ISBN (Print)9783319616087
    DOIs
    StatePublished - Jan 1 2017

    Fingerprint

    Computer vision
    Image processing
    Flavors
    Constrained optimization
    Signal processing
    Recovery
    Big data

    All Science Journal Classification (ASJC) codes

    • Computer Science(all)
    • Engineering(all)

    Cite this

    Monga, V., Mousavi, H. S., & Srinivas, U. (2017). Sparsity constrained estimation in image processing and computer vision. In Handbook of Convex Optimization Methods in Imaging Science (pp. 177-206). Springer International Publishing. https://doi.org/10.1007/978-3-319-61609-4_8
    Monga, Vishal ; Mousavi, Hojjat Seyed ; Srinivas, Umamahesh. / Sparsity constrained estimation in image processing and computer vision. Handbook of Convex Optimization Methods in Imaging Science. Springer International Publishing, 2017. pp. 177-206
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    Monga, V, Mousavi, HS & Srinivas, U 2017, Sparsity constrained estimation in image processing and computer vision. in Handbook of Convex Optimization Methods in Imaging Science. Springer International Publishing, pp. 177-206. https://doi.org/10.1007/978-3-319-61609-4_8

    Sparsity constrained estimation in image processing and computer vision. / Monga, Vishal; Mousavi, Hojjat Seyed; Srinivas, Umamahesh.

    Handbook of Convex Optimization Methods in Imaging Science. Springer International Publishing, 2017. p. 177-206.

    Research output: Chapter in Book/Report/Conference proceedingChapter

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    Monga V, Mousavi HS, Srinivas U. Sparsity constrained estimation in image processing and computer vision. In Handbook of Convex Optimization Methods in Imaging Science. Springer International Publishing. 2017. p. 177-206 https://doi.org/10.1007/978-3-319-61609-4_8