Matching of the Multi-channel Images with Improved Nonparametric Transformations and Weighted Binary Distance Measures

Abstract

This paper extends the concepts of image matching in the non-parametric space and binary distance measures. Matching in the nonparametric domain exhibits many desirable properties at relatively small computation complexity: It concentrates on capturing mutual relation among pixels in a small neighbourhoods rather than bare intensity values, thus improving matching discrimination. It is also more resistive against noise and uneven lighting conditions of the matched images. Last but not least, the matching algorithms operate in the integer domain and can be easily implemented in hardware what benefits in dramatic improvement of their run times. In this paper we extend the concept of nonparametric image transformation into the realm of colour images taking into consideration different colour spaces and different distances defined in these spaces. We propose significant bit reduction for aggregated block matching in the Census domain. We propose also the sparse sampling model for the Census transformation that increase the discriminative power of this representation and allows even further reduction of bits necessary for matching. The presented techniques have been applied to matching of the stereo images but can be employed in any computer vision task that requires comparison of images, such as image registration, object detection and recognition, etc. Presented experiments exhibit interesting properties of the described techniques.