Detection of obscured and partially covered objects using partial network matching and an image feature network-based object recognition algorithm
Jeremy Straub · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
An approach to image classification based on the analysis of the network of points generated by an image feature detection algorithm has been proposed. This network-based approach looks at the networks produced by two images and scale and then compare them, making a classification decision. This paper considers techniques to handle the problem posed by input images that are obscured or in which the target is partially covered. These approaches are compared with the base algorithm to assess the impact on performance in the general case, obscured scenarios and obstructed scenarios.