Self-organizing maps
Computer and Robot Vision
TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural network based defect inspection from images
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Recognition of Western style musical genres using machine learning techniques
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Automatic Classification of Wood Defects Using Support Vector Machines
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Application perspectives for the convolutional downward spiral architecture
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Clustering the ecological footprint of nations using Kohonen's self-organizing maps
Expert Systems with Applications: An International Journal
Neural network based defect inspection from images
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
Automatic sapstain detection in processed timber
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
A psycho-cognitive segmentation of organ donors in Egypt using Kohonen's self-organizing maps
Expert Systems with Applications: An International Journal
A neuro-computational intelligence analysis of the global consumer software piracy rates
Expert Systems with Applications: An International Journal
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
Visual detection of hexagonal headed bolts using method of frames and matching pursuit
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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The appearance of sawn timber has huge natural variations that the human inspector easily compensates for mentally when determining the types of defects and the grade of each board. However, for automatic wood inspection systems these variations are a major source for complication. This makes it difficult to use textbook methodologies for visual inspection. These methodologies generally aim at systems that are trained in a supervised manner with samples of defects and good material, but selecting and labeling the samples is an error-prone process that limits the accuracy that can be achieved. We present a non-supervised clustering-based approach for detecting and recognizing defects in lumber boards. A key idea is to employ a self-organizing map (SOM) for discriminating between sound wood and defects. Human involvement needed for training is minimal. The approach has been tested with color images of lumber boards, and the achieved false detection and error escape rates are low. The approach also provides a self-intuitive visual user interface.