The work presents a novel row-parallel architecture for a fast, high-resolution CMOS range sensor utilizing dynamic triangulation. The sensor detects the location of the structured light (e.g., laser line) in each row based on temporal signatures from the sensory signal. The range map is detected at twice the resolution than the photodetector count and is sampled on a 30 /spl times/ 30 /spl mu/m grid in the image plane. Our current prototype can operate up to 2000 frames per second, and deliver sub millimeter precision from a stand off distance of about 70 cm.

We describe a compound eye vision sensor for 3D ego motion computation. Inspired by eyes of insects, we show that the compound eye sampling geometry is optimal for 3D camera motion estimation. This optimality allows us to estimate the 3D camera motion in a scene-independent and robust manner by utilizing linear equations. The mathematical model of the new sensor can be implemented in analog networks resulting in a compact computational sensor for instantaneous 3D ego motion measurements in full six degrees of freedom.

Presents a new intensity-to-time processing paradigm suitable for very large scale integration (VLSI) computational sensor implementation of global operations over sensed images. Global image quantities usually describe images with fewer data. When computed at the point of sensing, global quantities result in a low-latency performance due to the reduced data transfer requirements between an image sensor and a processor. The global quantities also help global top-down adaptation: the quantities are continuously computed on-chip, and are readily available to sensing for adaptation. As an example, we have developed a sorting image computational sensor-a VLSI chip which senses an image and sorts all pixel by their intensities. The first sorting sensor prototype is a 21/spl times/26 array of cells. It receives an image optically, senses it, and computes the image's cumulative histogram-a global quantity which can be quickly routed off chip via one pin. In addition, the global cumulative histogram is used internally on-chip in a top-down fashion to adapt the values in individual pixel so as to reflect the index of the incoming light, thus computing an "image of indices". The image of indices never saturates and has a uniform histogram.

The need for low-latency vision systems is growing: high speed visual servoing and vision-based human computer interface. In this paper we present a new intensity-to-time processing paradigm suitable for low-latency massively parallel global computation over fine-grained data such as images. As an example of a low-latency global computation, we have developed a VLSI sorting computational sensor-a sensor which sorts all pixels of an input image by their intensities, as the image is being sensed. The first sorting sensor prototype is a 21 by 26 array of cells. It detects an image focused thereon and computes the image of indices as well as the image's cumulative histogram, before the intensity data are readout. The image of indices never saturates and has uniform histogram. Under user's control, the chip can perform other operations including simple segmentation and labeling.