Chemical machine vision: automated extraction of chemical metadata from raster images

Research output: Contribution to journalArticlepeer-review

Authors

Colleges, School and Institutes

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.

Details

Original languageEnglish
Pages (from-to)1342-1355
Number of pages14
JournalJ Chem Inf Comput Sci
Volume43
Issue number5
Publication statusPublished - 8 Aug 2003