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Abstract: Human hand shape features extraction from image frame sequences is one of the key steps in human hand 2D/3D tracking system and human hand shape recognition system. In order to satisfy the need of human hand tracking in real time, a fast and accurate method for acquirement of edge features from human hand images without consideration of hand over face is put forward in this paper. The proposed approach is composed of two steps, the coarse location phase (CLP) and the refined location phase (RLP) from coarseness to refinement. In the phase of CLP, the hand contour is approximately described by a polygon with concave and convex, an approach to obtaining hand shape polygon using locating points and locating lines is meticulously discussed. Then, a coarse location (CL) algorithm for extraction of interested hand shape features, such as contour, fingertips, roots of fingers, joints and the intersection of knuckle on different fingers, is proposed. In the phase of RLP, a multi-scale approach is introduced into our study to refine the features obtained by the CL algorithm. By means of defining the response strength of different types of features, a refined location (RL) algorithm is proposed. The major contribution of this paper is that the novel detection operators for features of hand images are presented in the above two steps, which have been successfully applied to our 3D hand shape tracking system and 2D hand shape recognition system. A number of comparative studies with real images and online videos demonstrate that the proposed method can extract the three defined human hand image features with high accuracy and high speed. [ABSTRACT FROM AUTHOR]

Coronal Mass Ejection (CME) is a major solar activity that affects the earth, thus CMEs detection is of great importance for space weather forecast, disaster prevention and reduction. We model the detection of Coronal Mass Ejections (CMEs) as the classification of the brightest block in the current running difference image. Because CMEs usually correspond to the areas with high gray values or complex texture features, multiple features including gray features and texture features are extracted to represent the brightest block. And classifier is designed based on these features. Our method includes four steps: first, because the CMEs spread along the radial direction of the sun, in order to facilitate the analysis, the original coordinate is transformed into the polar coordinate; Secondly, because the typical appearance of the CMEs is bright or complex texture enhancement, we use the brightest block to represent the whole image; Thirdly, we extract the gray, texture and HOG features of the brightest gray blocks. Finally, we use the extracted features to design decision trees as the base classifiers, and AdaBoost is used to obtain the final ensemble classifier. As far as we know, this is the first time that the learning based classification framework is presented in the CMEs detection. Moreover, multiple feature fusion is first used to model the various CMEs. Experimental results show that the integration of multi-feature based detection algorithm proposed can achieve better detection results. [ABSTRACT FROM AUTHOR]