Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
A New Method in Locating and Segmenting Palmprint into Region-of-Interest
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Genetic-based K-means algorithm for selection of feature variables
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Improving Nearest Neighbor Classifier Using Tabu Search and Ensemble Distance Metrics
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Adaptive surface inspection via interactive evolution
Image and Vision Computing
IEEE Transactions on Knowledge and Data Engineering
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Soft computing for automated surface quality analysis of exterior car body panels
Applied Soft Computing
Human-machine interaction issues in quality control based on online image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Assessment of the influence of adaptive components in trainable surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
A multilevel information fusion approach for visual quality inspection
Information Fusion
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Automated surface inspection of products as part of a manufacturing quality control process involves the applications of image processing routines to segment regions of interest (ROI) or objects which correspond to potential defects on the product or part. In these type of applications, it is not known in advance how many ROIs may be segmented from images, and so classification algorithms mainly make use of only image-level features, ignoring important object-level information. In this paper, we will investigate how to preprocess high-dimensional object-level features through a unsupervised learning system and present the outputs of that system as additional image-level features to the supervised learning system. Novel semi-supervised approaches based on K-Means/Tabu Search(TS) and SOM/Genetic Algorithm (GA) with C4.5 as supervised classifier have been proposed in this paper. The proposed algorithms are then applied on real-world CD/DVD inspection system. Results have indicated an increase in the performance in terms of classification accuracy when compared with various existing approaches.