Digital image processing
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Correspondence between different view breast X-rays using curved epipolar lines
Computer Vision and Image Understanding
Computer and Robot Vision
Crossing line profile: a new approach to detecting defects in aluminium die casting
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
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
Intelligent segmentation method for real-time defect inspection system
Computers in Industry
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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The Automated Multiple View Inspection (AMVI) has been recently developed for automated defect detection of manufactured objects. The approach detects defects by analysing image sequences in two steps. In the first step, potential defects are automatically identified in each image of the sequence. In the second step, the potential defects are tracked in the sequence. The key idea of this strategy is that only the existing defects (and not the false detections) can be successfully tracked in the image sequence because they are located in positions dictated by the motion of the test object. The AMVI strategy was successfully implemented for calibrated image sequences. However, it is not simple to implement it in industrial environments because the calibration process is a difficult task and unstable. In order to avoid the mentioned disadvantages, in this paper we propose a new AMVI strategy based on the tracking of potential detects in uncalibrated image sequences. Our approach tracks the potential defects based on a motion model estimated from the image sequence self. Thus, we obtain a motion model by matching structure points of the images. We show in our experimental results on aluminium die castings that the detection is promising in uncalibrated images by detecting 92.3% of all existing defects with only 0.33 false alarms per image.