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Edge Detection by Maximum Entropy: Application to Omnidirectional and Perspective Images

Edge Detection by Maximum Entropy: Application to Omnidirectional and Perspective Images

Ibrahim Guelzim, Ahmed Hammouch, El Mustapha Mouaddib, Driss Aboutajdine
Copyright: © 2013 |Pages: 14
ISBN13: 9781466639065|ISBN10: 1466639067|EISBN13: 9781466639072
DOI: 10.4018/978-1-4666-3906-5.ch011
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MLA

Guelzim, Ibrahim, et al. "Edge Detection by Maximum Entropy: Application to Omnidirectional and Perspective Images." Intelligent Computer Vision and Image Processing: Innovation, Application, and Design, edited by Muhammad Sarfraz, IGI Global, 2013, pp. 146-159. https://doi.org/10.4018/978-1-4666-3906-5.ch011

APA

Guelzim, I., Hammouch, A., Mouaddib, E. M., & Aboutajdine, D. (2013). Edge Detection by Maximum Entropy: Application to Omnidirectional and Perspective Images. In M. Sarfraz (Ed.), Intelligent Computer Vision and Image Processing: Innovation, Application, and Design (pp. 146-159). IGI Global. https://doi.org/10.4018/978-1-4666-3906-5.ch011

Chicago

Guelzim, Ibrahim, et al. "Edge Detection by Maximum Entropy: Application to Omnidirectional and Perspective Images." In Intelligent Computer Vision and Image Processing: Innovation, Application, and Design, edited by Muhammad Sarfraz, 146-159. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3906-5.ch011

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Abstract

In the edge detection, the classical operators based on the derivation are sensitive to noise which causes detection errors. It is even more erroneous in the case of omnidirectional images, due to geometric distortions caused by the used sensors. This paper proposes a statistical method of edge detection invariant to image resolution applied to omnidirectional images without preliminary treatments. It is based on the entropy measure. The authors compared its behavior with existing methods on omnidirectional images and perspectives images. The criteria of comparisons are the parameters of Fram and Deutsch. For omnidirectional images, the authors used two types of neighborhood: fixed and adapted to the parameters of the sensor. The authors compared the results of detection visually. The tests are performed on grayscale images.

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