High-order neural networks for image recognition

Okechukwu A. Uwechue; Abhijit S. Pandya; Peter Szabo

doi:10.1117/12.216358

11 August 1995 High-order neural networks for image recognition

Okechukwu A. Uwechue, Abhijit S. Pandya, Peter Szabo

Proceedings Volume 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing; (1995) https://doi.org/10.1117/12.216358
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

Abstract

This paper discusses the application of high-order neural networks (HONNs) for image recognition and image enhancement of digitized images. A key property of neural networks is their ability to recognize invariances and extract essential parameters from complex high- dimensional data. The most significant advantage of the HONN over first-order networks is that invariances to geometric transformations can be incorporated into the network and need not be learned through iterative weight updates. A third-order HONN can be used to achieve translation, scale, and rotation invariant recognition with a significant reduction in training time over other neural net paradigms such as the multilayer perceptron. We have developed a model based on a third-order net that can be trained with various images. Simulation results show that the model is able to perform very well with images embedded in noise. It is also shown that this method outperforms the Hamming net. Our model has also been applied to another difficult and computationally-complex problem: human face recognition. We put forth arguments for the use of isodensity information in the recognition algorithm. A method of image recognition that fuses isodensity information and neural networks is described and its merits over other image recognition methods are expounded. It is shown that isodensity information coupled with the use of an 'adaptive threshold' strategy yields a system that is to a high degree unperturbed by image contrast noise. Simulation results for these applications are presented in the paper.

Citation Download Citation

Okechukwu A. Uwechue, Abhijit S. Pandya, and Peter Szabo "High-order neural networks for image recognition", Proc. SPIE 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing, (11 August 1995); https://doi.org/10.1117/12.216358

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available