Real-time shape classification using biologically inspired invariant features

Over the past few decades, a considerable amount of literature has been published on shape classification. Since classification of well-segmented shapes has become easy to achieve, a number of recent studies have emphasized the importance of robustness to noise and deformations. So in this paper, we undertake the task of classifying similar & noisy binary shape images, using a biologically inspired technique called log-polar transform (LPT). The LPT mapping technique achieves scale and rotation invariance by simulating the foveal mechanism of the human vision system. In order to ensure optimal shape representation in the log-polar space, an iterative method is presented for the LPT lattice design. In addition to optimal shape representation, the use of linear discriminant analysis is proposed for dimensionality reduction and elimination of noisy features. Besides eliminating noisy features, discriminant analysis plays a crucial role in differentiating between similar shape categories. The proposed shape classification framework is tested on five publicly available databases, and substantial boost in classification accuracy is reported compared to state-of-the-art methods. In addition to superior classification accuracy, real time performance is demonstrated using an efficient PC-based implementation.