Title
Chemical Machine Vision: Automated Extraction of Chemical Metadata from Raster Images.
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.
Year
DOI
Venue
2003
10.1021/ci034017n
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
Keywords
Field
DocType
machine vision
Metadata,Computer vision,Feature vector,Raster graphics,Machine vision,Computer science,Gabor wavelet,Euclidean distance,Digital image,Self-organizing map,Artificial intelligence
Journal
Volume
Issue
ISSN
43
5
0095-2338
Citations 
PageRank 
References 
3
0.50
10
Authors
5
Name
Order
Citations
PageRank
Georgios V. Gkoutos139936.73
Henry S. Rzepa244575.96
Richard M. Clark3310.94
Osei Adjei4144.20
Harpal Johal530.50