Object Recognition Using Local Characterisation and Zernike Moments

Abstract. 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.

• Type: Conference
• Year: 2005
• Where: ACIVS
• Authors: Anant Choksuriwong, Hélène Laurent, Christophe Rosenberger, Choubeila Maaoui
• Keywords: ACIVS 2005, Computer Vision, Local Approach, Object Invariant Descriptors, Zernike Invariant Moments