An orientation detection model based on fitting from multiple local hypotheses

Authors:
Hui Wei;Yuan Ren
Affiliations:
Lab of Cognitive Model and Algorithm, School of Computer Science, Fudan University, Shanghai, China;Lab of Cognitive Model and Algorithm, School of Computer Science, Fudan University, Shanghai, China
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Year:
2012

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Abstract

Orientation detection is an important step in image understanding. Simple cells in the primary visual cortex are sensitive to stimuli of specific orientation. Hubel and Wiesel's classical theory attributes orientation selection in a simple cell to the aligned receptive fields of the retinal ganglion cells and the lateral geniculate nucleus cells. It is hypothesized that these receptive fields are similar in size and sub-structure. Current anatomical and electrophysiological evidence is not sufficient to support this geometric-arrangement-based model. Additionally, this feedforward model is incomplete in the implementation details of neural computation. The proposed algebraic model satisfies local constraints from afferent neurons and estimates the orientation of the stimulus. This constraint satisfaction based approach achieves desirable results on challenging image dataset, and can also be used to explain visual illusions.