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Self-Organizing Maps
NEFCLASS-X — a Soft Computing Tool to Build Readable Fuzzy Classifiers
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An Adaptive Texture and Shape Based Defect Classification
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Adaptive surface inspection via interactive evolution
Image and Vision Computing
Active Learning with Feedback on Features and Instances
The Journal of Machine Learning Research
Extensions of vector quantization for incremental clustering
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A framework for image classification
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Unsupervised texture classification: Automatically discover and classify texture patterns
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Feature Extraction & Image Processing, Second Edition
Image and Vision Computing
Increasing classification robustness with adaptive features
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Impact of object extraction methods on classification performance in surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
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BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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IEEE Transactions on Neural Networks
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Fuzzy Sets and Systems
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Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Information Sciences: an International Journal
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In this paper, we present image classifiers which are able to adapt and evolve themselves at an on-line machine vision system. These classifiers are initially trained on some pre-labelled training data and further updated based on newly recorded samples, for instance during a production process. The evolution and adaptation mechanism is necessary in order to guarantee a process-save on-line system as usually the pre-labelled data does not cover all possible operating conditions, system states or image classes. It is also recommended for a refinement of the classifiers during the on-line mode in order to boost predictive performance with more loaded samples. We will present two types of on-line evolving image classifiers: The first one is a clustering-based classification approach, which exploits conventional vector quantization, forming an incremental evolving variant around it and extending it to the supervised classification case. The second one is an evolving fuzzy classifier approach which comes with two model architectures, classical single model and a novel multi-model architecture, the later exploiting indicator matrices/vectors for training. The approaches are evaluated in three different on-line surface inspection systems dealing with CD imprint inspection, egg inspection and inspection of metal rotor parts. The evaluation will show the impact of on-line evolved versus 'static' classifiers kept fixed during the whole on-line process.