Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Affine Invariant Features from the Trace Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new fingerprint matching approach using level 2 and level 3 features
Proceedings of The Fourth International C* Conference on Computer Science and Software Engineering
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Even if lots of object invariant descriptors have been proposed in the literature, putting them into practice in order to obtain a robust system face to several perturbations is still a studied problem. Comparative studies between the most commonly used descriptors put into obviousness the invariance of Zernike moments for simple geometric transformations and their ability to discriminate objects. Whatever, these moments can reveal themselves insufficiently robust face to perturbations such as partial object occultation or presence of a complex background. In order to improve the system performances, we propose in this article to combine the use of Zernike descriptors with a local approach based on the detection of image points of interest. We present in this paper the Zernike invariant moments, Harris keypoint detector and the support vector machine. Experimental results present the contribution of the local approach face to the global one in the last part of this article.