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Total Variation Applications in Computer Vision

Total Variation Applications in Computer Vision

Vania Vieira Estrela, Hermes Aguiar Magalhães, Osamu Saotome
ISBN13: 9781466686540|ISBN10: 1466686545|EISBN13: 9781466686557
DOI: 10.4018/978-1-4666-8654-0.ch002
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MLA

Estrela, Vania Vieira, et al. "Total Variation Applications in Computer Vision." Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing, edited by Narendra Kumar Kamila, IGI Global, 2016, pp. 41-64. https://doi.org/10.4018/978-1-4666-8654-0.ch002

APA

Estrela, V. V., Magalhães, H. A., & Saotome, O. (2016). Total Variation Applications in Computer Vision. In N. Kamila (Ed.), Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing (pp. 41-64). IGI Global. https://doi.org/10.4018/978-1-4666-8654-0.ch002

Chicago

Estrela, Vania Vieira, Hermes Aguiar Magalhães, and Osamu Saotome. "Total Variation Applications in Computer Vision." In Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing, edited by Narendra Kumar Kamila, 41-64. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-8654-0.ch002

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Abstract

The objectives of this chapter are: (i) to introduce a concise overview of regularization; (ii) to define and to explain the role of a particular type of regularization called total variation norm (TV-norm) in computer vision tasks; (iii) to set up a brief discussion on the mathematical background of TV methods; and (iv) to establish a relationship between models and a few existing methods to solve problems cast as TV-norm. For the most part, image-processing algorithms blur the edges of the estimated images, however TV regularization preserves the edges with no prior information on the observed and the original images. The regularization scalar parameter ? controls the amount of regularization allowed and it is essential to obtain a high-quality regularized output. A wide-ranging review of several ways to put into practice TV regularization as well as its advantages and limitations are discussed.

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