Fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition

TL;DR

This paper introduces a novel object recognition method inspired by the human visual cortex, utilizing Fourier descriptors on the roto-translation group to achieve invariance to geometric transformations and improve classification accuracy.

Contribution

The paper presents a new global feature extraction technique based on Fourier descriptors on the V1 cortex-inspired group, enhancing object recognition performance.

Findings

Outperforms traditional descriptors in accuracy

Demonstrates robustness to noise

Effective on COIL-100 and ORL databases

Abstract

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.