99,561 research outputs found
Automated image inspection using wavelet decomposition and fuzzy rule-based classifier
A general purpose image inspecting system has been developed for automatic flaw detection in industrial applications. The system has a general purpose image understanding architecture that performs local feature extraction and supervised classification. Local features of an image are extracted from the compactly supported wavelet transform of the image. The features extracted from the wavelet transform provide local harmonic analysis and multi-resolution representation of the image. Image segmentation is achieved by classifying image pixels based on features extracted within a local area near each pixel. The supervised classifier used in the segmentation process is a fuzzy rule-based classifier which is established from the training data. The fuzzy rule base that is used to control the performance of the classifier is optimized by combining similar training data into the same rule. Therefore an optimization is achieved for the established rule base to provide the maximum amount of information with the minimum amount of rules. The experimental results show that the features extracted from the wavelet decomposition give contextual information for the test images. The optimized fuzzy rule-based classifier gives the best performance in both the training and the classification stages. Flaws in the test images are detected automatically by the computer
A robust approach to model-based classification based on trimming and constraints
In a standard classification framework a set of trustworthy learning data are
employed to build a decision rule, with the final aim of classifying unlabelled
units belonging to the test set. Therefore, unreliable labelled observations,
namely outliers and data with incorrect labels, can strongly undermine the
classifier performance, especially if the training size is small. The present
work introduces a robust modification to the Model-Based Classification
framework, employing impartial trimming and constraints on the ratio between
the maximum and the minimum eigenvalue of the group scatter matrices. The
proposed method effectively handles noise presence in both response and
exploratory variables, providing reliable classification even when dealing with
contaminated datasets. A robust information criterion is proposed for model
selection. Experiments on real and simulated data, artificially adulterated,
are provided to underline the benefits of the proposed method
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