Web Release Date: August 8,
Chemical Machine Vision: Automated Extraction of Chemical Metadata from Raster Images
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Wolfson Laboratory for Informatics, Modeling and Visualization, Department of Chemistry, Imperial College of Science, Technology & Medicine, Exhibition Road, South Kensington, London, England SW7 2AY, Department of Computing and Information Systems, University of Luton, Park Square, Luton, Bedfordshire, England, LU1 3JU, and Ultra Electronics Control Division, Greenford, Middlesex, England, UB6 8UA
Received January 29, 2003
Abstract:
We present a novel application of machine vision methods for the identification of chemical composition diagrams from two-dimensional digital raster images. The method is based on the use of Gabor wavelets and an energy function to derive feature vectors from digital images. These are used for training and classification purposes using a Kohonen network for classification with the Euclidean distance norm. We compare this method with previous approaches to transforming such images to a molecular connection table, which are designed to achieve complete atom connection table fidelity but at the expense of requiring human interaction. The present texture-based approach is complementary in attempting to recognize higher order features such as the presence of a chemical representation in the original raster image. This information can be used for providing chemical metadata descriptors of the original image as part of a robot-based Internet resource discovery tool.
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