Computationally Efficient Two Stage Sequential Framework for Stereo Matching

Almost all the existing stereo algorithms fall under a common assumption that corresponding color or intensity values will be similar like one another. On the other hand, it is also not that true in practice where the image color or intensity values are regularly affected by different radiometric factors like illumination direction, change in image device, illuminant color and so on. For this issue, the information about the raw color of the images which is recorded by the camera should not depend on it totally, and also the common assumptions on color consistency doesn’t influence good (great) between the stereo images in real scenario. Therefore, most of the conventional stereo algorithms can be seriously degraded in terms of performance under radiometric variations. In this work, we intend to develop a new stereo matching algorithm which will be insensitive to change in radiometric conditions between stereo pairs i.e. left image as well as right image. Unlike the other stereo algorithms, we propose a computationally efficient two stage sequential framework for stereo matching which can handle the various radiometric variations between the stereo pairs.Experimental results proves that the proposed method outperforms extremely well compare to other state of the art stereo methods under change in various radiometric conditions for a given stereo pair and it is also found from the results that the execution time is less compare to existing methods.