A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Hypothesizing and testing geometric properties of image data
CVGIP: Image Understanding
Maximum-likelihood edge detection in digital signals
CVGIP: Image Understanding
Segmentation of X-ray and C-scan images of fiber reinforced composite materials
Pattern Recognition
Sensor Modeling, Probabilistic Hypothesis Generation, and Robust Localization for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical Models and Image Processing
Graphical Models and Image Processing
Primitive Features by Steering, Quadrature, and Scale
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
SSPR '96 Proceedings of the 6th International Workshop on Advances in Structural and Syntactical Pattern Recognition
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
IEEE Transactions on Image Processing
A Visual Attention Operator Based on Morphological Models of Images and Maximum Likelihood Decision
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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Extraction of structural features in radiographic images is considered in the context of flaw detection with application to industrial and medical diagnostics. The known approache, like the histogram-based binarization yield poor detection results for such images, which contain small and low-contrast objects of interest on noisy background. In the presented model-based method, the detection of objects of interest is considered as a consecutive and hierarchical extraction of structural features (primitive patterns) which compose these objects in the form of aggregation of primitive patterns. The concept of relevance function is introduced in order to perform a quick location and identification of primitive patterns by using the binarization of regions of attention. The proposed feature extraction method has been tested on radiographic images in application to defect detection of weld joints and extraction of blood vessels in angiography.