An interface that involves all five senses, including olfaction, would be the ultimate interface for virtual reality (VR). We are trying to construct an olfactory display that does not require the user to wear anything on the face. We used an "air cannon" to transport small packets of scented air to the user's nose from some nearby place. In this paper, we report the ongoing development of an olfactory display system with a nose-tracking feature by incorporating vision-based face tracking technology.

Among head gestures, nodding and head-shaking are very common and used often. Thus the detection of such gestures is basic to a visual understanding of human responses. However it is difficult to detect them in real-time, because nodding and head-shaking are fairly small and fast head movements. We propose an approach for detecting nodding and head-shaking in real time from a single color video stream by directly detecting and tracking a point between the eyes, or what we call the "between-eyes". Along a circle of a certain radius centered at the "between-eyes", the pixel value has two cycles of bright parts (forehead and nose bridge) and dark parts (eyes and brows). The output of the proposed circle-frequency filter has a local maximum at these characteristic points. To distinguish the true "between-eyes" from similar characteristic points in other face parts, we do a confirmation with eye detection. Once the "between-eyes" is detected, a small area around it is copied as a template and the system enters the tracking mode. Combining with the circle-frequency filtering and the template, the tracking is done not by searching around but by selecting candidates using the template; the template is then updated. Due to this special tracking algorithm, the system can track the "between-eyes" stably and accurately. It runs at 13 frames/s rate without special hardware. By analyzing the movement of the point, we can detect nodding and head-shaking. Some experimental results are shown.

Head and face detection and eye tracking in real time are the first steps for head gesture recognition and/or face expression recognition for a human-computer interaction interface. We propose a two-step approach for eye tracking in video streams. First, we detect or track a point between the eyes. For this task, we apply a special filter that we have proposed previously (Proc. IEEE 4th Int. Conf. on Automatic Face and Gesture Recognition, pp. 40-45, 2000). Once we have detected the point between the eyes, it is fairly easy to locate the eyes, which are the two small darkest parts on each side of this point. Because detecting a point between the eyes is easier and more stable than detecting the eyes directly, the system can track the eyes robustly. We implemented the system on an SGI O2 workstation. The video image size is 320/spl times/240 pixels. The system processes images at seven frames per second in the detection mode and at 13 frames per second in the tracking mode, without any special hardware.

Describes a conic projection image sensor (COPIS) and its application: navigating a mobile robot in a manner that avoids collisions with objects approaching from any direction. The COPIS system acquires an omnidirectional view around the robot, in real-time, with use of a conic mirror. Based on the assumption of constant linear motion of the robot and objects, the objects moving along collision paths are detected by monitoring azimuth changes. Confronted with such objects, the robot changes velocity to avoid collision and determines locations and velocities.>

A conic projection image sensor (COPIS) and its application to guide the navigation of a mobile robot are described. The COPIS system acquires an omnidirectional view around the robot in real-time by using a conic mirror. Under the assumption of constant linear motion of the robot and moving objects, objects moving along collision paths are found by monitoring their azimuth changes. If such objects are found, the robot changes its velocity to avoid collision. Judgment as to whether each object is static or moving and estimation of its location and velocity are possible by the change of the robot's velocity.>