Analog VLSI and neural systems
Analog VLSI and neural systems
Analog hardware for detecting discontinuities in early vision
International Journal of Computer Vision
A delay-line based motion detection chip
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Investigations of analog VLSI visual transduction and motion processing
Investigations of analog VLSI visual transduction and motion processing
Compact Integrated Motion Sensor With Three-Pixel Interaction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Silicon retina with adaptive filtering properties
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
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
Computation of smooth optical flow in a feedback connected analog network
Proceedings of the 1998 conference on Advances in neural information processing systems II
Measurement of Visual Motion
Analysis and Design of Analog Integrated Circuits
Analysis and Design of Analog Integrated Circuits
Visual Motion Computation in Analog VLSI Using Pulses
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Analog Velocity Sensing Circuits Based on Bio-Inspired Correlation Neural Networks
MICRONEURO '99 Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems
An analog motion field detection chip for image segmentation
IEEE Transactions on Circuits and Systems for Video Technology
Homography Based State Estimation for Aerial Robots
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Robust bioinspired architecture for optical-flow computation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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I present a new focal-plane analog very-large-scale-integrated (aVLSI) sensor that estimates optical flow in two visual dimensions. Its computational architecture consists of a two-layer network of locally connected motion units that collectively estimate the optimal optical flow field. The applied gradient-based optical flow model assumes visual motion to be translational and smooth, and is formulated as a convex optimization problem. The model also guarantees that the estimation problem is well-posed regardless of the visual input by imposing a bias towards a preferred motion under ambiguous or noisy visual conditions. Model parameters can be globally adjusted, leading to a rich output behavior. Varying the smoothness strength, for example, can provide a continuous spectrum of motion estimates, ranging from normal to global optical flow. The non-linear network conductances improve the resulting optical flow estimate because they reduce spatial smoothing across large velocity differences and minimize the bias for reliable stimuli. Extended characterization and recorded optical flow fields from a 30 脳 30 array prototype sensor demonstrate the validity of the optical flow model and the robustness and functionality of the computational architecture and its implementation.