Hierarchical classification of surface defects on dusty wood boards
Pattern Recognition Letters
Image analysis for the biological sciences
Image analysis for the biological sciences
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
A Survey of Camera Self-Calibration
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A prototype vision system for analyzing CT imagery of hardwood logs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Defect detection in textured materials using optimized filters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimal Gabor filters for texture segmentation
IEEE Transactions on Image Processing
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
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We present a novel concept, the histogram of connected elements (HCE) which is a generalization of the usual gray-level histogram of digital images is introduced and its application to 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. Experimental results are obtained and we present a comparative study with several well-known and thoroughly tested techniques for the segmentation of textured images, including two algorithms belonging to the adaptive Bayesian family of restoration and segmentation methods and a probabilistic relaxation process.