Sparse window local stereo matching

We propose a new local algorithm for dense stereo matching of gray images. This algorithm is a hybrid of the pixel based and the window based matching approach; it uses a subset of pixels from the large window for matching. Our algorithm does not suffer from the common pitfalls of the window based matching. It successfully recovers disparities of the thin objects and preserves disparity discontinuities. The only criterion for pixel selection is the intensity difference with the central pixel. The subset contains only pixels which lay within a fixed threshold from the central gray value. As a consequence of the fixed threshold, a low-textured windows will use a larger percentage of pixels for matching, while textured windows can use just a few. In such manner, this approach also reduces the memory consumption. The cost is calculated as the sum of squared differences normalized to the number of the used pixels. The algorithm performance is demonstrated on the test images from the Middlebury stereo evaluation framework