Chemical machine vision: Automated extraction of chemical metadata from raster images
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Chemical machine vision: Automated extraction of chemical metadata from raster images. / Gkoutos, Georgios V; Rzepa, Henry; Clark, Richard M; Adjei, Osei; Johal, Harpal.
In: Journal of Chemical Information and Computer Sciences, Vol. 43, No. 5, 2003, p. 1342-1355.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Chemical machine vision: Automated extraction of chemical metadata from raster images
AU - Gkoutos, Georgios V
AU - Rzepa, Henry
AU - Clark, Richard M
AU - Adjei, Osei
AU - Johal, Harpal
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/2160/12071
U2 - 10.1021/ci034017n
DO - 10.1021/ci034017n
M3 - Article
C2 - 14502466
VL - 43
SP - 1342
EP - 1355
JO - Journal of Chemical Information and Computer Sciences
JF - Journal of Chemical Information and Computer Sciences
SN - 0095-2338
IS - 5
ER -