Wavelet Based Histogram of Oriented Gradients Feature Descriptors for Classification of Partially Occluded Objects
Автор: Ajay Kumar Singh, V. P. Shukla, Shamik Tiwari, Sangappa R. Biradar
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 3 vol.7, 2015 года.
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Computer vision applications face various challenges while detection and classification of objects in real world like large variation in appearances, cluttered back ground, noise, occlusion, low illumination etc.. In this paper a Wavelet based Histogram of Oriented Gradients (WHOG) feature descriptors are proposed to represent shape information by storing local gradients in image. This results in enhanced representation of shape information. The performance of the feature descriptors are tested on multiclass image data set having partial occlusion, different scales and rotated object images. The performance of WHOG feature based object classification is compared with HOG feature based classification. The matching of test image with its learned class is performed using Back Propagation Neural Network (BPNN) algorithm. Proposed features not only performed superior than HOG but also beat wavelet, moment invariant and Curvelet.
HOG, WHOG, Multiclass, Occlusion, Neural Network, Wavelet
Короткий адрес: https://sciup.org/15010668
IDR: 15010668
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