Hierarchical classification of surface defects on dusty wood boards
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
Digital Image Processing
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Texture Defect Detection Using Invariant Textural Features
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Comparative Evaluation of Texture Analysis Algorithms for Defect Inspection of Textile Products
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
3D surface analysis using coupled HMMs
Machine Vision and Applications
Adaptive surface inspection via interactive evolution
Image and Vision Computing
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Algorithm that mimics human perceptual grouping of dot patterns
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Human-machine interaction issues in quality control based on online image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On-line evolving image classifiers and their application to surface inspection
Image and Vision Computing
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
On-line incremental feature weighting in evolving fuzzy classifiers
Fuzzy Sets and Systems
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In surface inspection applications, the main goal is to detect all areas which might contain defects or unacceptable imperfections, and to classify either every single ‘suspicious’ region or the investigated part as a whole. After an image is acquired by the machine vision hardware, all pixels that deviate from a pre-defined ‘ideal’ master image are set to a non-zero value, depending on the magnitude of deviation. This procedure leads to so-called “contrast images”, in which accumulations of bright pixels may appear, representing potentially defective areas. In this paper, various methods are presented for grouping these bright pixels together into meaningful objects, ranging from classical image processing techniques to machine-learning-based clustering approaches. One important issue here is to find reasonable groupings even for non-connected and widespread objects. In general, these objects correspond either to real faults or to pseudo-errors that do not affect the surface quality at all. The impact of different extraction methods on the accuracy of image classifiers will be studied. The classifiers are trained with feature vectors calculated for the extracted objects found in images labeled by the user and showing surfaces of production items. In our investigation artificially created contrast images will be considered as well as real ones recorded on-line at a CD imprint production and at an egg inspection system.