Fuzzy Mathematical Technique to Bovine Feature Extraction and Identification

Abstract

A feature extractor which is rotation-invariant detector and descriptor, using fuzzy- SURF (Speeded-Up Robust Features) is presented. Fuzzy SURF approximates or even outperforms the schemes with respect to repeatability, distinctiveness, and robustness, which can be computed and compared much faster. Nose patterns are the asymmetrical features of the skin surface of bovine. The nose pattern can considered as a biometric identifier for bovine. This is done by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (specifically, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by matching with fuzzy similarity measure. This leads to a combination of novel detection, description, and matching steps. The paper encompasses a detailed description of the detector and descriptor and then explores the effects of the most important parameters