Analyzing images containing multiple sparse patterns with neural networks

  • Authors:

  • Rangachari Anand;Kishan Mehrotra;Chilukuri K. Mohan;Sanjay Ranka

  • Affiliations:

  • School of Computer and Information Science, Syracuse University, Syracuse, NY;School of Computer and Information Science, Syracuse University, Syracuse, NY;School of Computer and Information Science, Syracuse University, Syracuse, NY;School of Computer and Information Science, Syracuse University, Syracuse, NY

  • Venue:
  • IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
  • Year:
  • 1991

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Abstract

We have addressed the problem of analyzing images containing multiple sparse overlapped patterns. This problem arises naturally when analyzing the composition of organic macromolecules using data gathered from their NMR spectra. Using a neural network approach, we have obtained excellent results in using NMR data to analyze the presence of various amino acids in protein molecules. We have achieved high correct classification percentages (about 87%) for images containing as many as five substantially distorted overlapping patterns.