Measures based on fuzzy similarity for stereo matching of color images
In this article, new local matching measures, based on fuzzy similarity, for stereo matching of color images are proposed and evaluated. By formulating the individual similarity between a pair of pixels as a conjunction of the similarities of the respective color components, the field programmable gate array implementation problem becomes more computationally tractable, since the word-length of the numbers carrying the similarity information can be reduced compared to standard techniques (Sum of absolute differences and Sum of squared differences). It is also shown that combining information about color and local image structure (horizontal gradient component) in the matching measure and using a multi-scale measure is advantageous compared to using standard measures, in terms of percentage of correct matches and MSE for the erroneous matches. However, these improvements come at the prize of more complex implementations. Since the techniques are window-based, they share the typical drawbacks associated with other techniques of this type. This is expected, since the focus is on developing new local matching measure without addressing issues like post-processing of the resulting disparity maps. The techniques have been tested on two stereo color image pairs.
fuzzy similarity stereo matching local matching measure disparity