Composite filtering strategy for improving distortion invariance in object recognition

Abstract

Correlation-based pattern recognition filtering methods such as the eigenextended maximum average correlation height (EEMACH) filter is considered an effective tool in object recognition applications. However, these approaches require exclusive training for all possible distortions including in-plane as well as out-of-plane rotation, scale and illumination variations. The overall training process is exhaustive and requires training of filter banks to handle specific types of distortion separately. To overcome the aforementioned limitations, the authors propose a new difference of Gaussian (DoG)-based logarithmically preprocessed EEMACH filter which can manage multiple distortions in a single training instance while ensuring inherent control over illumination variations. The DoG-based logarithmic treatment exploits inherent capabilities of logarithmic preprocessing to manage scale and in-plane rotations. By reducing the number of classifier instances to one third, it not only reduces the computation complexity of the process to 33%, but also enhances the object recognition performance. The cumulative improvement is up to 2.73% in case of rotations and 10.8% in case of scaling by incorporating reinforced edges due to DoG operation. The resultant filter displays significantly enhanced recognition performance leading to a higher percentage of correct machine decisions, especially when an input scene contains multiple distortions.