TY - GEN N1 - this is a preprint/technical report that will be published soon in a journal. check http://www.cns.nyu.edu/~alan/publications/publications.htm for upcoming versions. ID - cogprints3377 UR - http://cogprints.org/3377/ A1 - Stocker, Dr. Alan TI - Integrated 2-D Optical Flow Sensor Y1 - 2004/01// N2 - I present a new focal-plane analog VLSI sensor that estimates optical flow in two visual dimensions. The chip significantly improves previous approaches both with respect to the applied model of optical flow estimation as well as the actual hardware implementation. Its distributed computational architecture consists of an array of locally connected motion units that collectively solve for the unique optimal optical flow estimate. The novel gradient-based motion model assumes visual motion to be translational, smooth and biased. The model guarantees that the estimation problem is computationally well-posed regardless of the visual input. 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. Unlike approaches that rely on the explicit matching of brightness edges in space or time, the applied gradient-based model assures spatiotemporal continuity on visual information. The non-linear coupling of the individual motion units improves the resulting optical flow estimate because it reduces spatial smoothing across large velocity differences. Extended measurements of a 30x30 array prototype sensor under real-world conditions demonstrate the validity of the model and the robustness and functionality of the implementation. AV - public KW - visual motion perception KW - 2-D optical flow KW - constraint optimization KW - gradient descent KW - aVLSI KW - analog network KW - collective computation KW - neuromorphic KW - feedback KW - nonlinear smoothing KW - perceptual prior ER -