Grant: Discovery Grants Program - Individual

It is expected that vision facilitation will play an important part in automotive safety, handheld devices, and wireless imaging. At the same time demands on imaging systems to reduce power and area are increasing. Consequently, novel solutions are required to achieve this goal. A conventional machine vision system has to sample a visual field at high speed. It also has to store large amounts of useless information, in the range of megabytes of data, for each frame. Useful information is buried under the raw data in each frame which, in turn, has to be heavily processed to extract it. For optical flow measurement, which is generally used for motion detection, the processing task is even more complicated since the optic flow is comprised of both spatial as well as temporal phenomena. This means that the vision system for optical flow measurement has to be designed to measure the spatio-temporal information, which requires a high number of computational tasks. In contrast to conventional vision systems, a dedicated vision chip is an integrated circuit that contains both image acquisition and processing. In this proposal, this type of vision chip is under investigation. The chip is going to perform pre-processing on the image, which means it will extract only useful data from each pixel. The front-end image processing on the image is performed using mixed-signal (analog and digital) circuits. The circuits and algorithms are inspired by biological models of insect vision. In insects, the eye element has a 2-D topology that is similar to pixels in the digital camera. However, the neural circuits in insects adapt to variable light and are useful for detecting moving objects. The insect vision model can be implemented by analog circuits, which in turn can effectively function in low contrast environments. Most importantly, this method allows circuitry to be implemented in a small package, which can be integrated into complex systems and serve as a pre-processing unit for sensor networks and handheld devices.