Object segmentation using skin color and motion information This paper presents an effective object segmentation method for objectoriented coding. The process is composed of facial region detection using skin color and changed region detection using motion information. The image is then segmented between moving objects and background using motion estimation. The facial regions are detected using skin color, the data for which is obtained from the u, v image. This data is then used as a threshold to segment the facial region. The image is also segmented into changed regions using motion information. After combining these results, the final image is segmented between moving objects and background. This method is more efficient due to two factors, color and motion. Experimental results show that the proposed method can significantly improve the image quality.

Face detection and tracking using edge orientation information An important topic in face recognition as well as in video coding or multimodal human machine interfaces is the automatic detection of faces or headandshoulder regions in visual scenes. The algorithms therefore should be computationally fast enough to allow an online detection and parallel processing of the detected objects. In this paper we describe our ongoing work on face detection using an approach that models the face appearance by edge orientation information. We will show that edge orientation is a powerful local image feature to describe objects like faces or body parts. We will present a simple and efficient method for template matching and object modeling based solely on edge orientation information. We also show how to obtain an optimal face model in the edge orientation domain from a set of training images. Unlike many approaches that model the grey level appearance of the face our approach is computationally very fast. It takes less than 0.1 seconds on a Pentium II 500MHz for a 190x140 image to be processed using a multiresolution search with three resolution levels. We demonstrate the capability of our detection method on an image database of 7000 images taken from more than 200 different people. The variations in head size, lighting and background are considerable. The obtained detection rate is more than 97% on that database. We have also extended the algorithm for face tracking. The tracking capabilities are shown using results from a real  time implementation of the proposed algorithm.