การรู้จำภาพใบหน้าโดยใช้หลายคุณลักษณะด้วยการประมวลผลกราฟแสดงค่าความถี่ของระดับความเข้ม

Identification and authentication by face recognition mainly use global face features. However, the recognition performance is not good. This research aims to develop a method to increase the efficiency of recognition using global-face feature and local-face feature with 4 parts: the left-eye, right-eye, nose and mouth. This method is based on geometrical techniques used to find location of eyes, nose and mouth from the frontal face image. We used 110 face images for learning and testing. The histogram processed face recognition technique is used. The results show that the recognition percentage is 89.09%

Analyzing Motion Parameters Using Unsupervised Fuzzy C-Prototypes

Motion-based segmentation plays an important role in dynamic scene analysis of video sequence. This technique should not only cluster the feature vectors but also extract the optimum number of clusters that correspond to the moving objects. The motion features of moving objects in a video sequence have to be extracted so that segmentation can be performed based on this information. In this paper, we present a scheme for extracting moving objects. First, the dense optical flow fields are calculated to extract motion vectors. Surface fitting is performed over the parametric motion model. Then, an unsupervised robust fuzzy C-Prototypes clustering technique is applied to motion-based segmentation in the parameter space. Finally, the individual moving object and background can be represented in layers. Experimental results showing the significance of ths proposed method are provided. 1 Introduction Recent technology in digital video processing has moved to "content-based" storage and retr..

Motion-based video segmentation using fuzzy clustering and classical mixture model

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Traffic Sign Recognition by Color Filtering and Particle Swarm Optimization

Abstract. In this paper a comprehensive approach to traffic sign detection and recognition is proposed. An RGB roadside image is acquired. Color filtering and segmentation is used to detect the boundary of traffic sign in binary mode. At the feature extraction stage, the RGB traffic sign region is cropped. The image is resized to 100x100 pixels. Finally, particle swarm optimization is used to identify the traffic sign. Experimental results show that our system can give a high recognition rate for all types of traffic signs used in Thailand: namely, prohibitory signs (red or blue), general warning signs (yellow) and construction area warning signs (amber)