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Proceedings Paper

Reconstruction of images using an artificial neural network with local-feature extraction
Author(s): Guoping Qiu
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Paper Abstract

In this work, we use artificial neural networks to study the problem of reconstructing visual images from their local features. An artificial neural network system with explicit local-feature extraction characteristics was devised to reconstruct visual images. The network studied was a multi-layer feed-forward network, it has a number of special neurons which are designed to resemble the complex and simple cells found in the biological visual systems. The neurons resembling the complex cells extract the lower frequency components of the image and the neurons resembling the simple cells extract the higher frequency components and edge information of the image. The output of these special neurons is forwarded to the higher layers of the network and the network learns to reconstruct the input image from these visually important local features. Experimental results show that excellent quality visual images can be reconstructed from only a few local features. We also discuss the potential applications of such a system to image data compression.

Paper Details

Date Published: 10 October 1994
PDF: 9 pages
Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); doi: 10.1117/12.188918
Show Author Affiliations
Guoping Qiu, Univ. of Derby (United Kingdom)


Published in SPIE Proceedings Vol. 2353:
Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision
David P. Casasent, Editor(s)

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