Tri-Stereo Image Matching using Watershed Segmentation on Disparity Space Image

Local stereo matching algorithms use winner-take-all approach to get the disparity. Many times they end up at an erroneous result. To solve this, in the present work, a novel stereo image matching technique has been developed that identifies the most likely local minimum from the several possible local minima that corresponds to true disparity. The technique uses properties of the physical continuity of the land surface by the watershed lines applied to the disparity space volume. This helps to minimize the search for the most probable local minima as the correct solution for the matching. The matching is further improved by combining the watershed lines of disparity space volume of two stereo pairs from the tri-stereo. In the present study, experiments have been carried out using the standard Middlebury stereo datasets and remotely sensed tri-stereo images. Based on this approach, the experiments are successfully carried out using the test dataset. The experimental results are compared with the results from the currently contemporary techniques of dynamic programming and semi-global matching which resulted in 2–10% improvement in density of matched points for different datasets.