We have developed a visual sensing system that monitors respiration for 24 hours non-restrictively. The principle of the system is inter-image subtraction. Compared with conventional methods, the system has advantages which make it suitable for a long-term monitoring: (1) patients experience no physiological burden during sleep, (2) no manual operation is necessary since it has self-optimizing functions for image processing and (3) it judges the patient's sleep condition adequately using statistical analysis. The system thus can, be applied to predict abnormality of health conditions, and thus for example, alert care providers so that action can be taken with a view to preventing the sudden death of elderly people or, in case of children, to prevent occurrence of sudden infant death syndrome. It can be also applied to the detect clinically dangerous sleep apnea syndrome. Experimental results in a nursing home for the aged have shown the efficiency of the system.

This paper is about automatically reconstructing the full 3D surface of an object observed in motion by a single static camera. We introduce the geotensity constraint that governs the relationship between four images of a moving object under fairly general lighting conditions. We show that it is possible in theory to solve for 3D surface structure for both the case of a single point light source and a pair of point, light sources and propose that a solution exists for an arbitrary number point light sources. The surface may or may not be textured. We then give an example of automatic surface reconstruction of a face under a point light source. The geotensity constraint provides the theoretical foundation for the full automatic 3D reconstruction of Lambertian objects using a single fixed camera, and arbitrary unknown object motion under arbitrary lighting conditions.

Automatic 3D model acquisition and 3D tracking of simple objects under motion using a single camera is often difficult due to the sparsity of information from which to establish the model. We have developed an automatic scheme that first computes a simple pointalistic Euclidean model of the object and then enriches this model using hyper-patches. These hyper-patches contain information on both the orientation and intensity pattern variation of roughly planar patches on an object. This information allows both the spatial and intensity distortions of the projected patch to be modelled accurately under 3D object motion. We show that hyper-patches not only can be computed automatically during model acquisition from a monocular image sequence, but that they are also extremely appropriate for the task of visual tracking.

This paper presents a new method for estimating locations of moving obstacles from stereo images without tracking between image frames and without searching corresponding points between stereo images. First, a free-space map in front of a vehicle is created by the planar projection stereopsis method which can easily decide whether or not each point in a stereo image exists on a ground plane. Next, the moving obstacles detected in an image by using the residual error calculated in the process of focus of expansion estimation are projected to the free-space map. In the free space map, obstacle locations are estimated as the intersection between the projected obstacle areas and the contour of a free-space area. Experimental results for real road scenes with pedestrian, bicycle or automobile as moving obstacles show the effectiveness of the proposed method.

This paper presents a new method which can effectively recognize moving objects by analyzing optical flow information acquired from dynamic images. This MOROFA (moving object recognition by optical flow analysis) method can be applied to many industrial areas; for example, an intelligent machine surveillance system or an obstacle detection system for an autonomous vehicle. At first, the optical flow field is detected in image sequences from a camera on a moving observer and moving object candidates are extracted by using the residual error value that is calculated in the process of estimating the focus of expansion. Next, the optical flow directions and intensity values are stored for the pixels involved in each candidate region to calculate the directions and the proportion values of the principal components. Finally, each candidate is classified into a category of object that is expected to appear in the scene by comparing the direction and the proportion values with standard data ranges for the objects which are determined by preliminary experiments. Experimental results of real outdoor scenes have shown the effectiveness of the proposed method.

The paper presents a simple moving obstacle detection method which used the residual error calculated in uses of focus of expansion (FOE) estimation. Method can be applied to many industrial tasks such as an intelligent machine surveillance system or an obstacle detection system for an autonomous vehicle system. First, the optical flow field is extracted from sequence of dynamic imaged captured by a fixed camera on a moving observer. Next, the FOE is estimated in local image regions. Its residual error is first calculated in the region. An image region corresponding to the block is added with the residual error. This process is repeated by sliding and adding for the local region, and changing the size of the local region. Finally, regions which have high residual error values are detected as candidate regions of moving obstacles. Experimental results using real outdoor scenes show the effectiveness of the proposed method.

This paper presents a new road extraction method for an autonomous vehicle which can acquire a road area by using height information of objects. Since a road area can be assumed to be a sequence of a flat plane in front of a vehicle, the road's height information is very effective for extracting a road area. For the purpose, the authors propose a new approach named the planar projection stereopsis (PPS) method which can easily decide whether each point in stereo images exists on the road plane or not. At first, PPS calculates a planar equation representing a road area by using height and pose of a camera in the vehicle. Next, stereo images are projected to the plane, where corresponding points are projected to the same positions on a certain road area if they really exist on a road plane, while corresponding points with different heights from the road plane are projected to different positions in each stereo image. Planar projection description is obtained by a subtraction between projected images from a set of stereo images and a road area can be represented by a set of points with small values. Experimental results for real road scenes have shown the effectiveness of the proposed method.

The authors present a navigation system using multiple sensors for unknown and dynamic indoor environments. To achieve robustness and flexibility in the mobile robot, a behavior-based system architecture is developed consisting of multilayered behaviors using multiple sensors that were ultrasonic sensors and a video camera. Basic behaviors required for navigation, such as avoiding obstacles, moving toward free space, and following targets are redundantly developed as agents and combined in a behavior-based system architecture. The capabilities of the system were demonstrated in unstructured real office environments using an indoor mobile robot.>

The authors are developing a compact cart-type robot system which moves around an office. They propose a behavior-based architecture with three clustered (reflexive, purposive, and adaptive) agents that realizes efficiency in attaining the mission of the robot, and robustness against the various kinds of failures that may occur in a dynamic environment. The reflexive-level group consists of agents with contact, infrared, and ultrasonic sensors which maintain minimal safety of the robot. The role of the purposive-level group is to achieve the global mission of the robot, such as 'if the robot detects a small fire in the office, find and reach a fire extinguisher as soon as possible'. The adaptive-level group stands by to recover from failure in the purposive-level group or in a deadlock situation. Experimental results showed the effectiveness of the method.>

We present a navigation system using multiple sensors for unknown and dynamic indoor environments. To achieve the robustness and flexibility of the mobile robot, we propose a new behavior-based architecture with three groups of clustered (reflexive, purposive, and adaptive) agents that realizes both efficiency in attaining the mission of the robot and robustness against the various kinds or failures that may occur in a dynamic environment. Basic behaviors required for navigation, such as, avoiding obstacles, moving towards free space, and following targets, are redundantly developed as agents and combined in the behavior-based system architecture.

A visual navigation system for a robot working in a nuclear power plant is presented. Such a system should have two functions. One is to provide a rough environment map or directions towards a goal, such as turning left at the second corner. The other is to avoid obstacles and to find a passage for continuous movement of the robot. The operation of the system is divided into two stages. In the first stage, a multi-information local map (MIL map) describing information necessary for self-location measurement is created interactively from stereo images collected during remote-controlled navigation. The second stage is autonomous navigation. The robot moves autonomously by using the map with the aid of the second function of the system. The system consists of five subsystems: an environment teaching subsystem, to create the MIL map; a self-location measurement subsystem, to find the robot's own location; an obstacle detection subsystem; a path planning subsystem; and a path following subsystem. Experimental results from a mobile robot show the usefulness of the system.>

This paper presents a newly developed estimation method for digital image restoration filters. Some degradation phenomena in images are mentioned and an idea to restore those degradations is presented in comparison to conventional techniques. An recursive estimation algorithm is discussed. The principal concept entailed in this algorithm is an adaptive estimation with an 'IDEAL IMAGE', which complies flexibly with filter size, evaluation criterion, and other parameters for iteration. An example of producing an 'IDEAL IMAGE' is also presented. The results of experimental studies on both preparatory and practical aspects are reported. The existence of ships in a satellite image of Yokohama Port appears clearer as a result of applying this method to the remotely sensed image data. Another practical result in digital radiographic images is demonstrated as well.