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

Abstract

Abstract-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.Index Terms-Disparity estimation, foreground fattening effect, global optimization, graph cut (GC), hierarchical bilateral disparity structure (HBDS), stereo matching.

Disparity refinement process based on RANSAC plane fitting for machine vision applications

“…Nevertheless, the methods above are produced foreground fattening effect when computing the original disparity map [14].…”

Stereo Matching Algorithm Based on Illumination Control to Improve the Accuracy

“…They are usually less sensitive to local individualities and tend to be computationally expensive. The measurement is taken from the global data and an additional smooth constraint for neighboring pixels (Wang et al, 2013a). The smooth constraint is utilized in order to preserve the disparity smoothness of the pixels of the same region while simultaneously refines the object boundaries.…”

Hybrid-Based Dense Stereo Matching

“…The smoothness term assigns a large penalty to those neighboring pixels conveying different disparity values, then the similar pixels can be merged. Various optimization techniques, such as graph cuts (Boykov et al, 2001;Wang et. al, 2013), belief propagation (BP) (Sun et al, 2003;Felzenszwalb and Huttenlocher, 2006;Klaus et al, 2006) and dynamic programming (DP) (Ohta and Kanade, 1985;Gong and Yang, 2003;Torr and Criminisi, 2004), are often used to determine the local minimum of the energy function.…”