A Rotation and Translation Invariant Discrete Saliency Network

We describe a neural network which enhances and completes salient closed contours. Our work is different from all previous work in three important ways. First, like the input provided to V1 by LGN, the input to our computation is isotropic. That is, the input is composed of spots not edges. Second, our network computes a well defined function of the input based on a distribution of closed contours characterized by a random process. Third, even though our computation is implemented in a discrete network, its output is invariant to continuous rotations and translations of the input pattern.

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: Lance R. Williams, John W. Zweck
• Keywords: Closed Contours, Input, NIPS 2001, NIPS 2007, Salient Closed Contours