Wood Texture Analysis by Combining the Connected Elements Histogram and Artificial Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Defect detection in textured materials using optimized filters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We present an automatic visual measurement and inspection system, currently operating at several European inspection plants. The main objective of the system is to automate the inspection of used wooden pallets. The paper begins with a brief description of the system, including some comments on the electromechanical handling of the inspected objects and the illumination set-up. Then, the paper presents the segmentation method used to extract the pallet elements, as an initial step for pallet measurements and the detection of possible defects. This method consists of an initial threshold on the histogram based on a Bayesian statistical classifier, followed by an iterative, heuristic search of the optimum threshold of the histogram. Finally, the paper introduces the application of the histogram of connected elements to the detection of very thin cracks, one of the hardest problems involved in the visual inspection of used pallets.