Adaptive Census and Interpolation Based Disparity Estimation Using Weighted Autoregressive

This paper deals with an adaptive general scale interpolation algorithm that is capable of arbitrary scaling factors considering the non-stationarity of natural images. The proposed AR terms are modeled by pixels with their adjacent unknown HR neighbors. A novel stereo matching algorithm is proposed that utilizes color segmentation on the reference image and a self-adapting matching score that maximizes the number of reliable correspondences. Modified Census Transform is a form of non-parametric local transform used in image processing within a square window to a bit string, thereby capturing the image structure. The centre pixel’s intensity value is replaced by the bit string composed of set of boolean comparisons such that in a square window, moving left to right. A new technique is used and found a solution for correspondence problem that makes use of non-parametric local transforms as the basis for correlation. Non-parametric transforms rely on the relative ordering of local intensity values, and not on the intensity values themselves. Correlation using such transforms can tolerate a significant number of images. This can result in improved performance near object boundaries when compared with conventional methods such as normalized correlation.