In this paper, we propose a progressive reliable points growing matching scheme to estimate the depth from the speckle projection image. First a self-adapting binarization is introduced to reduce the influence of inconsistent intensity. Then we apply local window-based correlation matching to get the initial disparity map. After the initialization, we formulate a progressive updating scheme to update the disparity estimation. There are two main steps in each round of updation. At first new reliable points are progressively selected based on three aspects of criterion including matching degree, confidence, and left-right consistency; then prediction-based growing matching is adopted to recalculate the disparity map from the reliable points. Finally, the more accurate depth map can be obtained by subpixel interpolation and transformation. The experimental results well demonstrate the effectiveness and low computational cost of our scheme.

In this paper, we propose a matting algorithm based on iterative transductive learning (for short: ITM). To avoid oversmooth results of recent methods, we introduce the influence of unlabeled regions as well as the consistency of neighboring pixels to re-design the optimization for alpha matting. A novel asymmetric Laplacian matrix is also proposed to further relieve the over-smoothness. To optimize the matting problem, we adjust the constrain coefficients between the initialized alpha matte and the asymmetric Laplacian matrix iteratively to achieve accurate alpha mattes. Consequently, during the iteration, high confidence pixels maintain their refined alpha values, whereas low confidence ones are updated by their neighbors gradually. Experimental results demonstrate that our algorithm is more precise than many state-of-the-art methods in terms of the accuracy.

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Static hand gesture recognition (HGR) has drawn increasing attention in computer vision and human-computer interaction (HCI) recently because of its great potential. However, HGR is a challenging problem due to the variations of gestures. In this paper, we present a new framework for static hand gesture recognition. Firstly, the key joints of the hand, including the palm center, the fingertips and finger roots, are located. Secondly, we propose novel and discriminative features called root-center-angles to alleviate the influence of the variations of gestures. Thirdly, we design a distance metric called finger length weighted Mahalanobis distance (FLWMD) to measure the dissimilarity of the hand gestures. Experiments demonstrate the accuracy, efficiency and robustness of our proposed HGR framework.

SUMMARYThis paper addresses stereo matching under scenarios of smooth region and obviously slant plane. We explore the flexible handling of color disparity, spatial relation and the reliability of matching pixels in support windows. Building upon these key ingredients, a robust stereo matching algorithm using local plane fitting by Confidence-based Support Window (CSW) is presented. For each CSW, only these pixels with high confidence are employed to estimate optimal disparity plane. Considering that RANSAC has shown to be robust in suppressing the disturbance resulting from outliers, we employ it to solve local plane fitting problem. Compared with the state of the art local methods in the computer vision community, our approach achieves the better performance and time efficiency on the Middlebury benchmark. key words: stereo matching, local based, local plane fitting, RANSAC