Robot vision
Analog VLSI and neural systems
Analog VLSI and neural systems
A delay-line based motion detection chip
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Characterization of subthreshold MOS mismatch in transistors for VLSI systems
Analog Integrated Circuits and Signal Processing - Joint special issue on analog VLSI computation
Compact Integrated Motion Sensor With Three-Pixel Interaction
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analog VLSI model of the fly elementary motion detector
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Robust Analog VLSI Motion Sensor Based on the Visual System of the Fly
Autonomous Robots
A CMOS Imager with On-Chip Temporal Filtering for Motion Pre-Processing
Analog Integrated Circuits and Signal Processing
Motion-Driven Segmentation by Competitive Neural Processing
Neural Processing Letters
High fill-factor imagers for neuromorphic processing enabled by floating-gate circuits
EURASIP Journal on Applied Signal Processing
Temporal order detection and coding in nervous systems
Neural Computation
| Hi-index | 0.00 |
Silicon imagers with integrated motion-detection circuitry have been developed and tested for the past 15 years. Many previous circuits estimate motion by identifying and tracking spatial or temporal features. These approaches are prone to failure at low SNR conditions, where feature detection becomes unreliable. An alternate approach to motion detection is an intensity-based spatiotemporal correlation algorithm, such as the one proposed by Hassenstein and Reichardt in 1956 to explain aspects of insect vision. We implemented a Reichardt motion sensor with integrated photodetectors in a standard CMOS process. Our circuit operates at sub-microwatt power levels, the lowest reported for any motion sensor. We measure the effects of device mismatch on these parallel, analog circuits to show they are suitable for constructing 2-D VLSI arrays. Traditional correlation-based sensors suffer from strong contrast dependence. We introduce a circuit architecture that lessens this dependence. We also demonstrate robust performance of our sensor to complex stimuli in the presence of spatial and temporal noise.