Corner Detection Using Fuzzy Principles

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Author(s): Erik Cuevas (Universidad de Guadalajara, Mexico), Daniel Zaldivar (Universidad de Guadalajara, Mexico)and Marco Perez-Cisneros (Universidad de Guadalajara, Mexico)
Copyright: 2012
Pages: 16
Source title: Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies
Source Author(s)/Editor(s): Vijay Kumar Mago (Simon Fraser University, Canada)and Nitin Bhatia (DAV College, India)
DOI: 10.4018/978-1-61350-429-1.ch015

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

Reliable corner detection is an important task in pattern recognition applications. In this chapter an approach based on fuzzy-rules to detect corners even under imprecise information is presented. The uncertainties arising due to various types of imaging defects such as blurring, illumination change, noise, et cetera. Fuzzy systems are well known for efficient handling of impreciseness. In order to handle the incompleteness arising due to imperfection of data, it is reasonable to model corner properties by a fuzzy rule-based system. The robustness of the proposed algorithm is compared with well known conventional detectors. The performance is tested on a number of benchmark test images to illustrate the efficiency of the algorithm in noise presence.

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