Digital neural networks are suitable for WSI implementation because their noise immunity is high, they have a fault tolerant structure, and the use of bus architecture can reduce the number of interconnections between neurons. To investigate the feasibility of WSIs, we integrated either 576 conventional neurons or 288 self-learning neurons on a 5-inch wafer, by using 0.8-µm CMOS technology and three metal layers. We also developed a new electron-beam direct-writing technology which enables easier fabrication of VLSI chips and wafer-level interconnections. We fabricated 288 self-learning neuron WSIs having as many as 230 good neurons.

We have developed a new disparity mapping technique for image morphing which prevents synthesized images from blurring and a fast rendering technique which realizes interactive morphing animation. In the image morphing rendering process, all pixels are moved according to their disparity maps and then distorted images are mixed with each other. Calculation costs of this process tend to be high because pixel per pixel moving and mixing are included. And if the accuracy of the disparity maps is low, synthesized images become blurred. This paper describes new two techniques for overcoming these problems. One is a disparity mapping technique by which the edges in each input image are accurately mapped to each other. This technique reduces blurring in synthesized images. The other is a data transformation technique by which the morphing rendering process is replaced with texture mapping, orthographic camera, α-brending and z-buffering. This transformation enables the morphing rendering process to be accelerated by 3D accelerators, thus enabling interactive morphing animations to be achieved on ordinary PCs.