Automated misplaced component inspection for printed circuit boards
Proceedings of the 21st international conference on Computers and industrial engineering
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Digital Image Processing
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
InsPulp-I©: an on-line visual inspection system for the pulp industry
Computers in Industry - Special issue: Machine vision
Automated vision system for localizing structural defects in textile fabrics
Pattern Recognition Letters
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
Design of an automatic wood types classification system by using fluorescence spectra
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This article presents improvements in the segmentation module, feature extraction module, and the classification module of a low-cost automated visual inspection (AVI) system for wood defect classification. One of the major drawbacks in the low-cost AVI system was the erroneous segmentation of clear wood regions as defects, which then introduces confusion in the classification module. To reduce this problem, we use the fuzzy min-max neural network for image segmentation (FMMIS). The FMMIS method grows boxes from a set of seed pixels, yielding ideally the minimum bounded rectangle for each defect present in the wood board image. Additional features with texture information are considered for the feature extraction module, and multi-class support vector machines are compared with multilayer perceptron neural networks in the classification module. Results using the FMMIS, additional features, and a pairwise classification support vector machine on a 550 test wood image set containing 11 defect categories show 91% of correct classification, which is significantly better than the original 75% of the low-cost AVI system. The use of computational intelligence techniques improved significantly the overall performance of the proposed automated visual inspection system for wood boards.