A collaborative decision-making model for orientation detection

Abstract

Orientation detection is a fundamental task for biological vision and machine vision. Hubel and Wiesel discovered the selectivity in a simple cell to stimulus of specific orientation, and proposed the famous feedforward model. The Hubel–Wiesel hypothesis attributes the orientation selectivity in a simple cell to the overlapping receptive field centers of its afferent LGN cells along a line, and therefore has several difficulties in the implementation. This paper proposes a collaborative decision-making approach of orientation detection using a double-layer neural network. The single estimation layer estimates the relative position of the contrast edge according to each bottom neuron's response to the contrast stimulus; and the collaborative-decision making layer determines the orientation by optimizing a least square with a unimodular constraint. This computational model cannot just account for orientation selectivity in a flexible way, but be applied to image processing. The statistical experiments found a satisfactory model configuration that balances the computational cost, effectiveness, and efficiency. The simulation experiments yield accurate results invariant to the contrast, and reasonably explain several visual illusions. Moreover, the proposed algorithm outperforms the related image processing algorithms on challenging natural images. The underlying neural mechanism of this model is compatible with the neurobiological findings, and is therefore appropriate for approaches of accomplishing higher level visual tasks.