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Proceedings Paper

Texture image segmentation using a structured artificial neural network
Author(s): Alex W. H. Lee; W. F. Tse; Lee Ming Cheng; L. L. Cheng
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Paper Abstract

Texture is one of the important characteristics used in identifying objects or regions of interest in an image. Statistical approach algorithms for image classifications are very poor techniques in identifying texture in particular the spatial gray level dependence method (SGLDM). The main disadvantage is the intensive computation required for this algorithm. The advantage of using ANN is less computational time once the network is trained and constructed in a parallel architecture. To improve the computational speed and parallelism further a structured ANN is used. Here, we will describe the use of this ANN for textured image segmentation. A structural artificial neural network with three sub-networks is proposed to estimate the SGLDM algorithm. A texture image segmentation system can be built by using this network and searching window method. The advantage of this design is that the ANN structure is a feed-forward network, so that the system can be built in a pile-line fashion. One of the applications can be the object searching in wafer or VLSI circuit inspection.

Paper Details

Date Published: 18 August 1997
PDF: 8 pages
Proc. SPIE 3185, Automatic Inspection and Novel Instrumentation, (18 August 1997); doi: 10.1117/12.284029
Show Author Affiliations
Alex W. H. Lee, City Univ. of Hong Kong (Hong Kong China)
W. F. Tse, City Univ. of Hong Kong (Hong Kong China)
Lee Ming Cheng, City Univ. of Hong Kong (Hong Kong China)
L. L. Cheng, City Univ. of Hong Kong (Hong Kong China)


Published in SPIE Proceedings Vol. 3185:
Automatic Inspection and Novel Instrumentation
Anthony Tung Shuen Ho; Sreenivas Rao; Lee Ming Cheng, Editor(s)

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