Fourier Descriptors Based on the Structure of the Human Primary Visual Cortex with Applications to Object Recognition

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• 作者：Amine Bohi ; Dario Prandi ; Vincente Guis…
• 关键词：Descriptor ; Fourier transform ; Hexagonal grid ; Geometric transformations ; Support vector machine ; Object recognition
• 刊名：Journal of Mathematical Imaging and Vision
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：57
• 期：1
• 页码：117-133
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Image Processing and Computer Vision; Applications of Mathematics; Signal,Image and Speech Processing; Mathematical Methods in Physics;
• 出版者：Springer US
• ISSN：1573-7683
• 卷排序：57

文摘

In this paper, we propose a supervised object recognition method using new global features and inspired by the model of the human primary visual cortex V1 as the semidiscrete roto-translation group \(SE(2,N) = {\mathbb {Z}}_N\rtimes {\mathbb {R}}^2\). The proposed technique is based on generalized Fourier descriptors on the latter group, which are invariant to natural geometric transformations (rotations, translations). These descriptors are then used to feed an SVM classifier. We have tested our method against the COIL-100 image database and the ORL face database, and compared it with other techniques based on traditional descriptors, global and local. The obtained results have shown that our approach looks extremely efficient and stable to noise, in presence of which it outperforms the other techniques analyzed in the paper.