Stereo correspondence using an assisted discrete cosine transform method

In this paper, a stereo matching algorithm using a window based frequency comparison method is formulated. The algorithm works with a local matching stereo model where a normalized cost function between frequency components and intensity values is used. The algorithm determines matching points in a stereo pair and uses a weighted cost function to determine the true disparity of the stereo pair. Unlike classical stereo correspondence algorithms that determine initial disparity maps through window based color intensity comparisons, the proposed algorithm uses window based frequency comparisons to exemplify the ability of frequency components to accurately find high detailed segments of the image. The algorithm is evaluated on the Middlebury data sets, and shows that it is noise and distortion resistant similar to the work in [1], thus allowing for higher reliability during comparisons. Additionally, this provides an advantage over typical color intensity comparisons as noise present in an image may cause mismatching when color intensity comparisons are executed.