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It is well known that it is difficult for local stereo algorithms to obtain correct disparities at occluded regions and depth discontinuities. In order to increase matching accuracy, current algorithms always use some disparity refinement measures. An adaptive algorithm to refine disparities based on color and distances has got good results but still not satisfactory. This paper proposes a simple...
The identification of bloodstains in color images is critical in some accident investigation cases. A suspected bloodstain segmentation method was proposed in this study. The algorithm transforms the RGB to the YCgCr color space after the image undergoes color histogram equalization. Determination of the decision thresholds (Cg, Cr) is based on the color distribution of the sample images in Cg-Cr...
Recently, local stereo matching has experienced large progress by the introduction of adaptive support-weights. In this paper, we aim at eliminating negative effects of occlusions by proposing an occlusion-based method to improve traditional support weights. Weights of occluded points are greatly reduced while computing matching costs, initial disparities and final disparities. Experimental results...
This paper provides a method for indoor semantic mapping in 3D environment. For indoor environment constructed by numerous planar surfaces, plane features are extracted and classified to build the main structure of indoor scene. To identify and cognize different objects located in indoor scene, both the position information and the color information are used in object classification. After the background...
Local stereo matching methods still play an important part as they are simple and fast. Some local methods perform well and even better than most global methods. But they usually achieve accuracy at the expense of speed. Simple local methods are fast, but exhibit systematic errors. In this paper, we focus on the invalid regions of traditional window-based matching and present a new solution to improve...
We empirically evaluate a distance-guided learning method embedded in a multiple classifier system (MCS) for tissue segmentation in optical images of the uterine cervix. Instead of combining multiple base classifiers as in traditional ensemble methods, we propose a Bhattacharyya distance based metric for measuring the similarity in decision boundary shapes between a pair of statistical classifiers...
Most existing methods of stereo matching focus on dealing with clear image pairs. Consequently, there is a lack of approaches capable of handling degraded images captured under challenging real situations, e.g. motion blur is present and an image pair is in different illumination conditions. In this paper we propose a novel approach to handling these challenging situations by formulating the problem...
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