In this project, we combined our interests to implement a graph cut algorithm for stereo correspondence and performed evaluation against a baseline algorithm using normalized cross correlation (NCC) across a variety of metrics.
Specifically, we investigated on the effectiveness of labeling disparities and handling occlusions for the graph cut algorithm.
We used a pre-aligned stereo image dataset with ground truth disparities from Middlebury College to benchmark performance.
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