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Although light field data provides abundant cues for depth estimation, light field depth estimation suffers from occlusion and uncertain edges. In this paper, we propose occlusion robust light field depth estimation using segmentation guided bilateral filtering. First, we calculate refocused images from light field data using digital refocusing. Second, we perform support vector machines (SVM) classification...
Stereo matching is a challenging problem and highly accurate depth image is important in different applications. The main problem is to estimate the correspondence between two pixels in a stereo pair. To solve this problem, in the last decade, several cost aggregation methods aimed at improving the quality of stereo matching algorithms have been introduced. We propose a new cost aggregation method...
Accuracy and computational complexity are challenges of stereo matching algorithm. Much research has been devoted to stereo matching based on cost volume filtering of matching costs. Local stereo matching based guided image filtering (GIF) has a computational complexity of O(N). A proposed algorithm focuses on reduction of computational complexity using the concept of fast guided image filter, which...
Formulated as a pixel-labeling problem, optical flow estimation using efficient edge-aware filtering has shown great success recently. However, the typical challenge that restricts the range of applicability of this method is the computational complexity mainly caused by the testing of every hypothetical label in the whole label space, which is usually large in an optical flow estimation. In this...
We propose a real-time upsampling scheme for depth maps. The proposed scheme contains two upsampling stages; one is selfsimilarity matching (SSM), and the other is predictive linear upsampling (PLU). SSM accelerates cost volume filtering by using a variant of joint bilateral upsampling, which utilizes highdimensional vectors, which is neighborhoods of an RGB image and a depth map. The high-dimensional...
Though many tasks in computer vision can be formulated elegantly as pixel-labeling problems, a typical challenge discouraging such a discrete formulation is often due to computational efficiency. Recent studies on fast cost volume filtering based on efficient edge-aware filters have provided a fast alternative to solve discrete labeling problems, with the complexity independent of the support window...
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