立體視覺匹配(stereo matching)藉由尋找兩視角影像之對應點，逕而估算視差資訊，但可能因為物件遮蔽、深度不連續，同質區域、光線而造成對應不準，無法獲得準確的視差資訊，雖然近年來已發展許多全域和局部的最佳化方法，改進對應不準的問題，但在物件遮蔽區域的大區塊的對應錯誤仍無法以最佳化方法解決，所以遮蔽區域偵測與視差修正變得相當重要。 因此，本論文提出基於匹配代價之非對稱式立體匹配遮蔽偵測演算法，其中包含三項重點，第一，我們利用適應性支持權重演算法(adaptive support-weight algorithm)計算初始匹配代價，以獲得較為準確的視差資訊，第二，我們以非對稱遮蔽偵測為主架構提出匹配代價之遮蔽偵測，並且結合geometry-based uniqueness constraint使遮蔽偵測能以較少計算時間達到一定的準確率。最後，我們在多階層式信息傳遞演算法以加重訊息傳遞的權重於遮蔽改善。實驗結果顯示，我們所提出的基於匹配代價之非對稱式立體匹配遮蔽偵測，相較於left/right checking failure可達到有較低的false negative rate (FNR)，且相較於geometry-based uniqueness constraint可達到有較低的false positive rate (FPR)，而結合多階層式可信度傳遞演算法，的確能有效改善遮蔽區域的視差資訊。 Stereo matching uses two images from different viewpoints to find corresponding points to estimate disparity (depth). However, stereo matching may lead to mismatching due to object occlusion, depth discontinuity, homogonous region and light effect. Although many local and global optimization methods have been proposed to solve the mismatching problem, of them can solve the mismatching error. Thus, occlusion detection and handling of a wide range of errors are important issues in stereo matching. Therefore, this paper proposes an asymmetric occlusion detection algorithm using matching cost for stereo matching, which includes three main points. First, we use adaptive support-weight algorithm to compute the initial matching cost, which improves the accuracy of disparity map. Secondly, we propose asymmetric occlusion detection using matching cost, and combine the geometry-based uniqueness constraint to reduce computation time and to achieve accurate detection. Finally, we further handle occlusion by increasing the weight of message propagation in hierarchical belief propagation. Our experimental results show that our proposed method obtains lower false positive rate (FPR) than left/right checking failure, and lower false positive rate (FPR) than geometry-based uniqueness constraint. Moreover, we adopt the hierarchical belief propagation algorithm to refine disparities in occluded regions.
