Narrow Results

Moving object tracking plays an important role in applications of object based video conference, video surveillance and so on. The computational complexity is very important in real-time object tracking. We assumed that the background scene is obtained before an object appears in the image and a camera moves after the object is detected. The proposed method can segment an object by using the difference image if there is no camera motion. After camera motion, it can track the object by using the backward BMA (block matching algorithm) with the HFM (human figure model). For real-time tracking, we used the ROI (region of interest) which is the tightest rectangle of the object. The simulation results show that the proposed method efficiently recognizes and tracks the moving camera as well as the fixed camera.

This paper presents a fast palmprint verification system based on fractal coding. In the stage of registration, a sub-image from user's training palmprints is intentionally extracted and stored as his or her template. In the stage of verification, the step of region of interest extraction is not needed, the sample image is directly matched with the template based on fractal coding, which can reduce the whole response time. Whether the sample image and the template are from the same person or not is decided by their matching scores. Experimental evaluation results on two databases clearly demonstrate the effectiveness of the proposed approach.