In Gradient-Based Cross-Spectral Stereo Matching (GB-CSSM) output disparity maps tend to produce coarse results that are, for the most part, reliable. However, general methods of improving the performance of disparity maps generated from the Cross-Spectral comparison of visual and full infrared input images are non-existent. In particular, previous works fail to address the role and interaction of the parameters present in a GB-CSSM algorithm as a way to improve the performance of a disparity map. In this paper, we introduce the first comprehensive comparison of GB-CSSM generated disparity maps. More specifically, we consider all possible input parameter combinations to a GB-CCSM algorithm and evaluate how these parameters affect runtime as well as accuracy and validity of the disparity maps. Our objective is to provide designers with a systematic means of classifying and easily identifying optimal disparity maps (in terms of a combination of runtime, accuracy, and validity). Our experimental results show how a Pareto frontier of optimal disparity maps can be generated as a result of our analysis. The ultimate goal is to allow for the development of new and improved GB-CSSM algorithms that can be applied to a broader range of applications.
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