Computing motion using analog VLSI vision chips: An experimental comparison among different approaches

We have designed, built and tested a number of analog CMOS VLSI circuits for computing 1-D motion from the time-varying intensity values provided by an array of on-chip phototransistors. We present experimental data for two such circuits and discuss their relative performance. One circuit approximates the correlation model while a second chip uses resistive grids to compute zero-crossings to be tracked over time by a separate digital processor. Both circuits integrate image acquisition with image processing functions and compute velocity in real time. For comparison, we also describe the performance of a simple motion algorithm using off-the-shelf digital components. We conclude that analog circuits implementing various correlation-like motion algorithms are more robust than our previous analog circuits implementing gradient-like motion algorithms

An Analog VLSI Chip for Finding Edges from Zero-crossings

We have designed and tested a one-dimensional 64 pixel, analog CMOS VLSI chip which localizes intensity edges in real-time. This device exploits on-chip photoreceptors and the natural filtering properties of resistive networks to implement a scheme similar to and motivated by the Difference of Gaussians (DOG) operator proposed by Marr and Hildreth (1980). Our chip computes the zero-crossings associated with the difference of two exponential weighting functions. If the derivative across this zero-crossing is above a threshold, an edge is reported. Simulations indicate that this technique will extend well to two dimensions. 1 INTRODUCTION The zero-crossings of the Laplacian of the Gaussian,r 2 G, are often used for detecting edges. Marr and Hildreth (1980) argued that the Mexican-hat shape of the r 2 G operator can be approximated by the difference of two Gaussians (DOG). In this spirit, we have built a chip that takes the difference of two resistive-network smoothings of photorece..