Enhanced target recognition employing spatial correlation filters and affine scale invariant feature transform

Abstract

A spatial domain optimal trade-off Maximum Average Correlation Height (SPOT-MACH) filter has been shown to have advantages over frequency domain implementations of the Optimal Trade-Off Maximum Average Correlation Height (OR-MACH) filter as it can be made locally adaptive to spatial variations in the input image background clutter and normalized for local intensity changes. This enables the spatial domain implementation to be resistant to illumination changes. The Affine Scale Invariant Feature Transform (ASIFT) is an extension of previous feature transform algorithms; its features are invariant to six affine parameters which are translation (2 parameters), zoom, rotation and two camera axis orientations. This results in it accurately matching increased numbers of key points which can then be used for matching between different images of the object being tested. In this paper a novel approach will be adopted for enhancing the performance of the spatial correlation filter (SPOT MACH filter) using ASIFT in a pre-processing stage enabling fully invariant object detection and recognition in images with geometric distortions. An optimization criterion is also be developed to overcome the temporal overhead of the spatial domain approach. In order to evaluate effectiveness of algorithm, experiments were conducted on two different data sets. Several test cases were created based on illumination, rotational and scale changes in the target object. The performance of correlation algorithms was also tested against composite images as references and it was found that this results in a well-trained filter with better detection ability even when the target object has gone through large rotational changes.