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Correlation based similarity measures-Summary

6 years 8 months ago
Correlation based similarity measures-Summary
Correlation based matching typically produces dense depth maps by calculating the disparity at each pixel within a neighborhood. This is achieved by taking a square window of certain size around the pixel of interest in the reference image and finding the homologous pixel within the window in the target image, while moving along the corresponding scanline. The goal is to find the corresponding (correlated) pixel within a certain disparity range d (d E [0,….,dmax]) that minimizes the associated error and maximizes the similarity. In brief, the matching process involves computation of the similarity measure for each disparity value, followed by an aggregation and optimization step. Since these steps consume a lot of processing power, there are significant speed-performance advantages to be had in optimizing the matching algorithm.
Added 01 Jul 2010
Updated 09 Dec 2010
Authors siddhant ahuja
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