A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An automatic assessment scheme for steel quality inspection
Machine Vision and Applications
Using moment invariants and HMM in facial expression recognition
Pattern Recognition Letters
Measuring shape: ellipticity, rectangularity, and triangularity
Machine Vision and Applications
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A machine vision inspector for beer bottle
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Fabric defect detection based on multiple fractal features and support vector data description
Engineering Applications of Artificial Intelligence
Neural network model-based automotive engine air/fuel ratio control and robustness evaluation
Engineering Applications of Artificial Intelligence
Intuition, Insight, Imagination and Creativity
IEEE Computational Intelligence Magazine
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Object type recognition for automated analysis of protein subcellular location
IEEE Transactions on Image Processing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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This paper presents a Computational Intelligence scheme to deal with subjective human inspection tasks in the industry that are subjective measurements. The scheme is used to solve two cosmetic subjective measurements tasks, classification of cosmetic defects and detection of non-uniform color regions in a translucent film. The first problem is solved with two approaches supervised and unsupervised Artificial Neural Networks. Both techniques yield the same performance, 92.35% of correct classification. Considering that a human inspector has a performance between 85% and 90%, the performance achieved is acceptable. The second problem is faced with a hybrid system based on fuzzy clustering and a Self-Organizing Map. The hybrid approach involves management of uncertainty through fuzzy theory and unsupervised training supported by the SOM. The proposed system is able to find non-uniform color regions with better resolution than a human inspector. The system also showed to be more sensitive than a simple fuzzy clustering approach.