Traffic sign recognition usually consists of two stages: detection and classification. In this paper, we describe the classification stage using the ring-partitioned method. The proposed method uses a specified grayscale image in the pre-processing step and ring-partitioned matching in the matching step. The method does not need carefully prepared many samples of traffic sign images for the training process, alternatively only the standard traffic signs are used as the reference images. The experimental results show the effectiveness of the method in the matching of occluded, rotated, and illumination problems of the traffic sign images with the fast computation time.

A family of new generalized LR parsing algorithms are proposed which make use of a set of ancestors tables introduced by Kipps. As Kipps's algorithm does not give us a method to extract any parsing results, his algorithm is not considered as a practical parser but as a recognizer. In this paper, we will propose two methods to extract all parse trees from a set of ancestors tables in the top vertices of a graph-structured stack. For an input sentence of length n, while the time complexity of the Tomita parser can exceed O(n3) for some context-free grammars (CFGs), the time complexity of our parser is O(n3) for any CFGs, since our algorithm is based on the Kipps's recognizer. In order to extract a parse tree from a set of ancestors tables, it takes time in order n2. Some preliminary experimental results are given to show the efficiency of our parsers over Tomita parser.

This paper describes a new color segmentation based on a normalized RGB chromaticity diagram for face detection. Face skin is extracted from color images using a coarse skin region with fixed boundaries followed by a fine skin region with variable boundaries. Two newly developed histograms that have prominent peaks of skin color and non-skin colors are employed to adjust the boundaries of the skin region. The proposed approach does not need a skin color model, which depends on a specific camera parameter and is usually limited to a particular environment condition, and no sample images are required. The experimental results using color face images of various races under varying lighting conditions and complex backgrounds, obtained from four different resources on the Internet, show a high detection rate of 87%. The results of the detection rate and computation time are comparable to the well known real-time face detection method proposed by Viola-Jones [11],[12].