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 marketing research, it is important to know what customers are interested in, and to evaluate the effectiveness of advertisements. Nowadays, marketing information regarding the evaluation of advertisements is mainly obtained from researchers' activities, which are difficult and time consuming. The research activities can be improved by use of surveillance systems with computer vision technologies. In this paper, a marketing information system that detects customers' attention behavior with computer vision is proposed. Cameras are attached to the display that presents advertisements in the system. Thus, the proposed method mainly consists of frontal face detection with "Four-Directional Features (FDF)" and calculating the spatial position of a face by stereovision. It is supposed that this system enables long time surveillance and uniform evaluation of presented advertisements. In addition, it is easy to distinguish which advertisements attract the most attention. Experimental results show that this system can detect attention behavior.

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.

This article discusses face and hand sign recognition and its applications. We have already proposed a method that can recognize faces and hand signs using hierarchical discriminant analysis. Here, we implement this method on a laptop computer and develop a real-time system that can control home appliances by recognizing faces and hand signs. Our system can control various appliances in the home by remote control. Moreover, since our system can recognize the user, it can assign distinct commands to the hand signs of each user and limit the appliances and functions that users can control.

We propose a multi-camera system that can track multiple human faces and hands as well as focus on face and hand gestures for recognition. Our current system consists of four cameras. Two fixed cameras are used as a stereo system to estimate face and hand positions. The stereo camera detects faces and hands by a standard skin color method we propose. The distances of the targets are then estimated. Next to track multiple targets, we estimate the positions and sizes of targets between consecutive frames. The other two cameras perform tracking of such targets as faces and hands. If a target is not the appropriate size for recognition, the tracking cameras acquire its zoomed image. Since our system has two tracking cameras, it can track two targets at the same time. To recognize faces and hand gestures, we propose four directional features by using linear discriminant analysis. Using our system, we experimented on human position estimation, multiple face tracking, and face and hand gesture recognition. These experiments showed that our system could estimate human position with the stereo camera and track multiple targets by using target positions and sizes even if the persons overlapped with each other. In addition, our system could recognize faces and hand gestures by using the four directional features.