Modeling Foveal Human Image Processing for Enhanced Contrast and Edge Detection of Images Used in Artificial Vision Systems

Abstract

The following paper describes an artificial neural network model that is inspired by human image processing. the proposed model attempts to integrate certain aspects of human image processing to incorporate the initial intelligent image processing necessary to allow artificial vision systems to function more efficiently in noisy and unknown environments. by incorporating the robust dynamic interactions between amacrine and bipolar cells, in addition to center-surround antagonism and photoreceptor blurring, the improved model demonstrates enhanced edge detection and contrast enhancement, in addition to providing gain control and noise reduction for the output response of the retina model. Real camera images were used to demonstrate the performance of the model. the model performance is compared with traditional image processing algorithms to illustrate the desired properties of the improved models.

Department(s)

Mathematics and Statistics

Second Department

Engineering Management and Systems Engineering

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 American Society of Mechanical Engineers, All rights reserved.

Publication Date

01 Dec 2003

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