Digital image processing
ISCGAV'08 Proceedings of the 8th conference on Signal processing, computational geometry and artificial vision
A template model for defect simulation for evaluating nondestructive testing in X-radiography
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Selecting an appropriate segmentation method automatically using ANN classifier
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Intelligent segmentation method for real-time defect inspection system
Computers in Industry
Automated multiple view inspection based on uncalibrated image sequences
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Advances on automated multiple view inspection
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
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Radioscopy is the accepted way for controlling the quality of aluminium die cast pieces through computer-aided analysis of X-ray images. Two classes of regions are possible in a digital X-ray image of a casting: regions belonging to regular structures of the specimen, and those relating to defects. Since the contrast between a flaw and a defectfree neighbourhood is distinctive, the detection is usually performed by thresholding this feature. Nevertheless, this measurement suffers from accuracy error when the neighbourhood is not homogeneous, for example when the flaw is at an edge of a regular structure of the test object. For this reason, many approaches use a-priori information about the location of regular structures of the test piece. In this paper, a new approach to detecting defects without a-priori knowledge is proposed. The approach is based on features extracted from crossing line profiles, i.e., the grey level profiles along straight lines crossing each segmented potential flaw in the middle. The profile that contains the most similar grey levels in the extremes is selected. Hence, the homogeneity of the neighbourhood is ensured. Features from the selected profile are extracted. The detection performance of our features and a vast number of other known features are assessed by computing the area Az under the Receiver Operation Characteristic (ROC) curve. The best performance is achieved using one of the proposed features yielding an area Az = 0.9944 in 50 X-ray images of aluminium wheels with 23.000 potential flaws.