Image-based rendering has been successfully used to display 3-D objects for many applications. A well-known example is the object movie, which is an image-based 3-D object composed of a collection of 2-D images taken from many different viewpoints of a 3-D object. In order to integrate image-based 3-D objects into a chosen scene (e.g., a panorama), one has to meet a hard challenge--to efficiently and effectively remove the background from the foreground object. This problem is referred to as multiview images (MVIs) segmentation. Another task requires MVI segmentation is image-based 3-D reconstruction using multiview images. In this paper, we propose a new method for segmenting MVI, which integrates some useful algorithms, including the well-known graph-cut image segmentation and volumetric graph-cut. The main idea is to incorporate the shape prior into the image segmentation process. The shape prior introduced into every image of the MVI is extracted from the 3-D model reconstructed by using the volumetric graph cuts algorithm. Here, the constraint obtained from the discrete medial axis is adopted to improve the reconstruction algorithm. The proposed MVI segmentation process requires only a small amount of user intervention, which is to select a subset of acceptable segmentations of the MVI after the initial segmentation process. According to our experiments, the proposed method can provide not only good MVI segmentation, but also provide acceptable 3-D reconstructed models for certain less-demanding applications.

This paper addresses real time feature tracking when the frame-to-frame motion is large in the video. To solve this problem, we first estimate the locally piecewise, translational motion by matching the horizontal and vertical characteristic curves of the consecutive images. Then, we incorporate the motion estimates into the pyramidal Kanade-Lucas-Tomasi (KLT) feature tracker to accomplish the tracking task. To compute the motion estimates efficiently and effectively, we use dynamic programming to minimize the cost function. These motion estimates will serve as the coarse motion at the deepest pyramid level which makes the residual motion small enough such that the feature tracker can work well. In addition, we introduce a feature rejection method that improves the efficiency. Experiments show that our method can make the feature tracker suitable to track features with large motion.