Determining Surface Orientation by Projecting a Stripe Pattern
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Quality of Training Sample Estimates of the Bhattacharyya Coefficient
IEEE Transactions on Pattern Analysis and Machine Intelligence
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Small Sample Error Rate Estimation for k-NN Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of pattern recognition & computer vision
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Surface Inspection using Coupled HMMs
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dependency-based feature selection for clustering symbolic data
Intelligent Data Analysis
A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties
IEEE Transactions on Computers
A Unified Approach to Feature Selection and Learning in Unsupervised Environments
IEEE Transactions on Computers
Journal on Image and Video Processing - Regular
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This paper proposed an image content description method within the context of specular surface inspection. Such a method is based on a preliminary research concerning the generation of specific stripe patterns for the visual enhancement of defective surface parts of cylindrical specular objects. The goal of this paper is to address the stripe pattern interpretation within a general approach. For this purpose, different pattern recognition processes, consisting not only of the combination of different image segmentation, feature retrieval, and classification, but also of feature combination and selection, will be considered. Three top-down and one bottom-up approaches are evaluated for retrieving the most appropriate feature sets in terms of highest classification rates. It will be demonstrated that following a combination and appropriate selection of these feature sets, even better rates can be reached. With only half of the initial features, an increase of more than 2% is observable.