JingHua Wang

Learn More
This paper combine two conventional feature extraction methods (NWFE&NPE) in a novel framework and present a new semi-supervised feature extraction method called Adjusted Semi supervised Discriminant Analysis (ASEDA). The advantage of this method is dominating the Hughes phenomena, automatic selection of unlabelled pixels, extraction of more than L-1(L:(More)
Kernel principal component analysis (KPCA) could extract nonlinear features from samples, however, its feature extraction efficiency is inversely proportional to the size of the training sample set. This paper proposes an efficient KPCA method that is much faster than the KPCA in extracting features from samples. The proposed method first selects nodes from(More)
Human objects segmentation is one of key problems of visual analysis. In this paper, a novel touched human objects segmentation based on mean shift algorithm is proposed. At first, video images is preprocessed and foreground objects (BLOB) is obtained, model of human object is built according to statistical characteristics of body surface. Then, a few of(More)
  • 1