This paper presents a full-body gesture database which contains 2D video data and 3D motion data of 14 normal gestures, 10 abnormal gestures and 30 command gestures for 20 subjects. We call this database the Korea University Gesture(KUG) database. Using 3D motion cameras and 3 sets of stereo cameras, we captured 3D motion data and 3 pairs of stereo-video data at 3 different directions for normal and abnormal gestures. In case of command gestures, 2 pairs of stereo-video data is obtained by 2 sets of stereo cameras with different focal length in order to effectively capture views of whole body and upper body, simultaneously. In addition to these, the 2D silhouette data is synthesized by separating a subject and background in 2D stereo-video data and saved as binary mask images. In this paper, we describe the gesture capture system, the organization of database, the potential usages of the database and the way of obtaining the KUG database.
In this paper, we present an e cient video parsing method using shot boundary detection and camera operation analysis technique. In the shot boundary detection, the local color information and an adaptive time window is used. The local spatio-temporal images and MLPmultilayer perceptron are used for analyzing the camera o p erations.In order to verify the performance of the proposed video p arsing method, experiments with video database have been carried out. Experimental results demonstrate the e ciency of the video p arsing technique.
Abstract. This paper proposes a method for face reconstruction that makes use of only a small set of feature points. Faces can be modeled by forming linear combinations of prototypes of shape and texture information. With the shape and texture information at the feature points alone, we can achieve only an approximation to the deformation required. In such an under-determined condition, we find an optimal solution using a simple least square minimization method. As experimental results, we show well-reconstructed 2D faces even from a small number of feature points.
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