Automated visual inspection: 1981 to 1987
Computer Vision, Graphics, and Image Processing
A survey of automated visual inspection
Computer Vision and Image Understanding
Digital image processing
A robust algorithm to estimate the fundamental matrix
Pattern Recognition Letters
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
Computer and Robot Vision
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Trilinearity of three perspective views and its associated tensor
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Robust automated multiple view inspection
Pattern Analysis & Applications
Crossing line profile: a new approach to detecting defects in aluminium die casting
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Automated multiple view inspection based on uncalibrated image sequences
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
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Automated inspection usingmultiple views (AMVI) has been recently developed to automatically detect flaws in manufactured objects. The principal idea of this strategy is that, unlike the noise that appears randomly in images, only the flaws remain stable in a sequence of images because they remain in their position relative to the movement of the object being analyzed. This investi- gation proposes a new strategy, based on the detection of flaws in a non- calibrated sequence of images. The method uses a scheme of elimination of potential flaws in two and three views. To improve the performance, intermediate blocks are introduced that eliminate those hypothetical flaws that are regular regions and real flaws. Use is made of images captured in a non-calibrated vision system, so there are no optical, geometric and noise disturbances in the image, forcing the proposed method to be robust, so that it can be applied in industry as a quality control method in non-calibrated vision systems. the results show that it is possible to detect the real flaws and at the same time decrease most of the false alarms.