Automatic Multiple Visual Inspection on Non-calibrated Image Sequence with Intermediate Classifier Block

Abstract

Automated inspection using multiple 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 investigation 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.

Active vision in robotic systems: A survey of recent developments

“…Related techniques are useful to improve the productivity of assembly lines (Park et al 2006). Self-reconfiguration Villalobos 2007a, 2007b) and self-calibration (Carrasco and Mery 2007;Treuillet et al 2009) are also mentioned in some applications. Further information regarding early literatures can be found in the review by Newman and Jain (1995).…”

“…Bayesian reasoning and classification methods are used in active perception for object recognition, search, surface reconstruction, and object modeling (Carrasco and Mery 2007;Mason 1997). Sutton and Stark also applied function-based reasoning for goaloriented image segmentation (Sutton and Stark 2008) and Zhang et al proposed a Bayesian network approach to sensor modeling and path planning (Zhang et al 2009).…”