In this paper, we propose a novel method to estimate the slant of omnidirectional image sensors which can acquire images of an environment with a FOV close to 360/spl deg/ /spl times/ 180/spl deg/. The proposed method consists of two steps: a low resolution voting followed by a least squares method (LSM). First, the direction of each edge pixel in the omnidirectional image is computed and projected to the plane of Z = 1. The biggest peak formed by the vertical edges, which are prevalent in indoor and urban scenes, is detected. Then, edge directions whose projections to the voting plane pass near the peak are used to estimate the slant of the sensor by LSM. Experimental results in a real environment show the effectiveness of the proposed method.

This paper describes a novel algorithm for robust object detection and segmentation, which is based on a new robust dissimilarity measure called as radial reach correlation (RRC). The capability of detecting moving objects from a complex background is one of the most fundamental technology for intelligent surveillance systems. The RRC is a new robust dissimilarity measure and has a well-formed probabilistic model of binary or normal density. The RRC evaluates the local texture between a background image and the current scene and realize robust object detection under poor conditions. To demonstrate the effectiveness of our approach, we present experimental results from real world all-directional images provided by the stereo omni-directional system (SOS).

For most of the existing pointing systems proposed in the past, the pointing target was limited to a specific surface in the space, usually an object on a 2-D screen. This paper presents a new solution for estimating the pointing target inside a 3-D space, such as a room. A special set of stereo cameras acquires images and depth maps from the entire environment. Using a background subtraction method, our system detects the foreground pixels, analyzes them and extracts the user's face and hand regions. Considering that the pointing direction is the straight line connecting the face and the hand, the pointing target can be seen as the intersection of that line with the 3-D model of the environment, allowing the calculation and the display of the person's viewpoint.

In this research, we introduce a reasonable noise model for range data which is obtained by a laser radar range finder, and derive two simple approximate solutions of optimal local plane fitting the range data under the noise model. We compare our methods with general least-squares based methods, such as Z-function fitting, the eigenvalue method, the maximum likelihood estimation method, and the renormalization method, an iterative method to obtain the optimal fitting of planes of range data under the noise model. All the methods are compared and evaluated using both synthetic range data and real range data with ground truth. From the experimental evaluation results, the proposed methods are shown to be effective, and the general least-squares-based methods are shown to be unsuitable for the assumed noise model.

In order to develop a shape and decorative pattern database of old pottery and ceramics objects, it is necessary to obtain the surface pattern on the 3-dimensional shape of the object. This paper describes the recovery of the revolution surface from a monocular image of unknown camera parameters and the retrieval of the 2-dimensional pattern. The camera parameters are obtained by using a genetic algorithm (GA). After the revolution surfaces are reconstructed, these surfaces are developed into a 2-dimensional plane. We show that a scanner-digitized image of an old ceramic object can be analyzed by GA to reconstruct the revolution surface and to develop the 2-dimensional image on the 3-dimensional object.