Search

A new area-based stereo matching in hierarchical framework is proposed. Local methods generally measure the similarity between the image pixels using local support window. An appropriate support window, where the pixels have similar disparity, should be selected adaptively for each pixel. Our algorithm consists of the following two steps. In the first step, given an estimated initial disparity map, we obtain an object boundary map for distinction of homogeneous/object boundary region. It is based on the assumption that the depth boundary exists inside of intensity boundary. In the second step for improving accuracy, we choose the size and shape of window using boundary information to acquire the accurate disparity map. Generally, the boundary regions are determined by the disparity information, which should be estimated. Therefore, we propose a hierarchical structure for simultaneous boundary and disparity estimation. Finally, we propose post-processing scheme for removal of outliers. The algorithm does not use a complicate optimization. Instead, it concentrates on the estimation of a optimal window for each pixel in improved hierarchical framework, therefore, it is very efficient in computational complexity. The experimental results on the standard data set demonstrate that the proposed method achieves better performance than the conventional methods in homogeneous regions and object boundaries. [ABSTRACT FROM AUTHOR]

We propose a fast disparity estimation algorithm using background registration and object segmentation for stereo sequences from fixed cameras. Dense background disparity information is calculated in an initialization step so that only disparities of moving object regions are updated in the main process. We propose a real-time segmentation technique using background subtraction and inter-frame differences, and a hierarchical disparity estimation using a region-dividing technique and shape-adaptive matching windows. Experimental results show that the proposed algorithm provides accurate disparity vector fields with an average processing speed of 15 frames/sec for 320x240 stereo sequences on a common PC. [ABSTRACT FROM AUTHOR]

This paper describes a 3D face recognition method using facial shape indexes. Given an unknown range image, we extract invariant facial features based on the facial geometry. We estimate the 3D head pose using the proposed error compensated SVD method. For face recognition method, we define and extract facial shape indexes based on facial curvature characteristics and perform dynamic programming. Experimental results show that the proposed method is capable of determining the angle of faces accurately over a wide range of poses. In addition, 96.8% face recognition rate has been achieved based on the proposed method with 300 individuals with seven different poses. [ABSTRACT FROM AUTHOR]

We propose and describe a novel video capturing system called Sat-Cam that can observe and record the users΄ activity from effective viewpoints to negate the effects of unsteady mobile video cameras or switching between a large number of video cameras in a given environment. By using real-time imagery from multiple cameras, our system generates virtual views fixed in relation to the object. The system consists of a capturing and segmentation components, a 3D modeling component, and a rendering component connected over a network. Results indicate that a scene rendered by Sat-Cam provides stable scenes that help viewers understand the activity in real time. By using the 3D modeling technique, an occlusion problem in object tracking is solved, allowing us to generate scenes from any direction and zooming in/out condition. [ABSTRACT FROM AUTHOR]