Table For visual comparison we present the disparity

4. Experimental results
In this LB Agar Miller section, the experimental settings is introduced first, the speed and accuracy evaluation is presented afterwards.
4.1. Experimental settings
Two typical refinement methods are compared with the proposed refinement method: non-local refinement method [13] (denoted as Nlocal) and weighted media filter method [16] (denoted as WMF).
In order to evaluate refinement performance on different cost aggregation methods, each disparity refinement method is evaluated on three aggregation methods: two complicated aggregation methods (guided filter aggregation method [11], non-local aggregation method [13]) and one simple box-filter aggregation method [1].
The experiments are carried on the Middlebury stereo benchmark [12]. In order to reliably evaluate performance on different texture stereo pairs, image we evaluate all the pairs that have ground-truths available. We consider the error metric as the percentage of bad pixels with error threshold 1.
Fig. 3. The proposed refinement result with MST edge weight clip shows higher accuracy. (a) Left image of baby3 dataset with textureless bottom region (shown in blue rectangle). (b)(c) disparity groundtruth and box-filter [1] aggregation result. (d) non-local refinement [13] result of (c). (e) and (g) are support weight from one stable pixel (shown in green) to other pixels without and with MST edge weight clip, the former decrease faster than the latter. (f) and (h) are the proposed refinement result of (c) without and with MST edge weight clip. In textureless bottom region, the proposed refinement result with weight clip shows better accuracy. (For interpretation of the references to color in esophagus figure legend, the reader is referred to the web version of this article.)Figure optionsDownload full-size imageDownload high-quality image (772 K)Download as PowerPoint slide
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