Improved depth map estimation in Stereo Vision

In this paper, we present a new approach for dense stereo matching which is mainly oriented towards the recovery of depth map of an observed scene. The extraction of depth information from the disparity map is well understood, while the correspondence problem is still subject to errors. In our approach, we propose optimizing correlation based technique by detecting and rejecting mismatched points that occur in the commonly challenging image regions such as textureless areas, occluded portions and discontinuities. The missing values are completed by incorporating edges detection to avoid that a window contains more than one object. It is an efficient method for selecting a variable window size with adaptive shape in order to get accurate results at depth discontinuities and in homogeneous areas while keeping a low complexity of the whole system. Experimental results using the Middlebury datasets demonstrate the validity of our presented approach. The main domain of applications for this study is the design of new functionalities within the context of mobile devices.