Problem Of Disparity Estimation Research Articles

Efficient Disparity Estimation Using Hierarchical Bilateral Disparity Structure Based Graph Cut Algorithm With a Foreground Boundary Refinement Mechanism

The disparity estimation problem is commonly solved using graph cut (GC) methods, in which the disparity assignment problem is transformed to one of minimizing global energy function. Although such an approach yields an accurate disparity map, the computational cost is relatively high. Accordingly, this paper proposes a hierarchical bilateral disparity structure (HBDS) algorithm in which the efficiency of the GC method is improved without any loss in the disparity estimation performance by dividing all the disparity levels within the stereo image hierarchically into a series of bilateral disparity structures of increasing fineness. To address the well-known foreground fattening effect, a disparity refinement process is proposed comprising a fattening foreground region detection procedure followed by a disparity recovery process. The efficiency and accuracy of the HBDS-based GC algorithm are compared with those of the conventional GC method using benchmark stereo images selected from the Middlebury dataset. In addition, the general applicability of the proposed approach is demonstrated using several real-world stereo images.

Stereovision depth analysis by two-dimensional motion charge memories

Several strategies to retrieve depth information from a sequence of images have been described so far. In this paper a method that turns around the existing symbiosis between stereovision and motion is introduced; motion minimizes correspondence ambiguities, and stereovision enhances motion information. The central idea behind our approach is to transpose the spatially defined problem of disparity estimation into the spatial–temporal domain. Motion is analyzed in the original sequences by means of the so-called permanency effect and the disparities are calculated from the resulting two-dimensional motion charge maps. This is an important contribution to the traditional stereovision depth analysis, where disparity is got from the image luminescence. In our approach, disparity is studied from a motion-based persistency charge measure.