An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Wood inspection with non-supervised clustering
Machine Vision and Applications
The Effect of Illumination Variations on Color-Based Wood Defect Classification
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Wood defects classification using a SOM/FFP approach with minimum dimension feature vector
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
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This paper addresses the issue of automatic wood defect classification. We propose a tree-structure support vector machine (SVM) to classify four types of wood knots by using images captured from lumber boards. Simple and effective features are proposed and extracted by first partitioning the knot images into 3 distinct areas, followed by applying an order statistic filter to yield an average pseudo color feature in each area. Excellent results have been obtained for the proposed SVM classifier that is trained by 800 wood knot images. Performance evaluation has shown that the proposed SVM classifier has resulted in an average classification rate of 96.5% and false alarm rate of 2.25% over 400 test knot images. Our future work includes more extensive tests on large data set and the extension of knot types.