Extraction of welds from radiographic images using fuzzy classifiers
Information Sciences—Informatics and Computer Science: An International Journal
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A new evolutionary algorithm for image segmentation
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Object detection for computer vision using a robust genetic algorithm
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Adaptive integrated image segmentation and object recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Classification of weld flaws with imbalanced class data
Expert Systems with Applications: An International Journal
Defects detection in X-ray images and photos
NEHIPISIC'11 Proceeding of 10th WSEAS international conference on electronics, hardware, wireless and optical communications, and 10th WSEAS international conference on signal processing, robotics and automation, and 3rd WSEAS international conference on nanotechnology, and 2nd WSEAS international conference on Plasma-fusion-nuclear physics
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With base in object detection and recognition techniques, we developed and implemented a new methodology to perform the first head-function of a weld quality interpretation system: the weld bead extraction from a digital radiograph. The proposed methodology uses a genetic algorithm to manage the search for suitable parameters values (position, width, length, and angle) that best defines a window, in the radiographic image, matching with the model image of a weld bead sample. The search results are verified in a classification process that recognize true detections using image matching parameters also proposed in this work. To test the proposed methodology, two groups of images were used; one consisting of 110 radiographs from pipelines welded joints and the other containing 6 images with different numbers of radiographs per image. The tests results showed that, besides automatically check the number of weld beads per image, the proposed methodology is also able to supply the respective position, width, length, and angle of each weld bead, with an accurate rate of 94.4%. As a result, the detected weld beads are correctly extracted from the original image and made available to be inspected through others algorithms for failure detection and classification.