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An improved 2D optical flow sensor for motion segmentation

By alan stocker


A functional focal-plane implementation of a 2D optical flow system is presented that detects an preserves motion discontinuities. The system is composed of two different network layers of analog computational units arranged in a retinotopical order. The units in the first layer (the optical flow network) estimate the local optical flow field in two visual dimensions, where the strength of their nearest-neighbor connections determines the amount of motion integration. Whereas in an earlier implementation \cite{Stocker_Douglas99} the connection strength was set constant in the complete image space, it is now \emph{dynamically and locally} controlled by the second network layer (the motion discontinuities network) that is recurrently connected to the optical flow network. The connection strengths in the optical flow network are modulated such that visual motion integration is ideally only facilitated within image areas that are likely to represent common motion sources. Results of an experimental aVLSI chip illustrate the potential of the approach and its functionality under real-world conditions

Topics: Computational Neuroscience, Artificial Intelligence, Machine Vision, Neural Nets, Robotics
Year: 2002
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  1. (1997). A focal plane visual motion measurement sensor,” Trans.
  2. (1986). An integrated analog optical motion sensor,”
  3. (1996). Analog VLSI motion discontinuity detectors for image segmentation,” in Intl.
  4. (1993). Bump circuits,”
  5. (1999). Computation of smooth optical flow in a feedback connected analog network,”
  6. (1988). Computing motion using analog and binary resistive networks,”
  7. (2001). Constraint Optimization Networks for Visual Motion Perception - Analysis and Synthesis,
  8. (1985). Neural computation of decisions in optimization problems,”
  9. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,”