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.

Describes a technique of gray-scale character recognition that offers both noise tolerance and affine-invariance. The key ideas are twofold. First is the use of normalized cross-correlation as a matching measure to realize noise tolerance. Second is the application of global affine transformation (GAT) to the input image so as to achieve affine-invariant correlation with the target image. In particular, optimal GAT is efficiently determined by the successive iteration method using topographic features of gray-scale images as matching constraints. We demonstrate the high matching ability of the proposed GAT correlation method using gray-scale images of numerals subjected to random Gaussian noise and a wide range of affine transformation. Moreover, extensive recognition experiments show that the achieved recognition rate of 94.3 percent against rotation within 30 degrees, scale change within 30 percent, and translation within 20 percent of the character width along with random Gaussian noise is sufficiently high compared to the 42.8 percent offered by simple correlation.

This paper presents a realtime pedestrian counting system based on x-t spacetime image analysis. The system counts the number of pedestrians through the combination of three processes: moving object extraction, object counting by region labeling, and direction detection by flow estimation. In order to extract objects reliably under various illumination changes, we propose new moving objects extraction methods that extract moving regions whose shadows have been eliminated by using adaptive background image reconstruction and the color information processing of the images. For labeling and flow estimation, we use the Ortho-sectioning method that analyzes extracted regions on a spacetime slice image of the original three dimensional volume. Experimental results confirm the robustness of the system against illumination changes.