19 May 2011 Integration of low level and ontology derived features for automatic weapon recognition and identification
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This paper presents a further step of a research toward the development of a quick and accurate weapons identification methodology and system. A basic stage of this methodology is the automatic acquisition and updating of weapons ontology as a source of deriving high level weapons information. The present paper outlines the main ideas used to approach the goal. In the next stage, a clustering approach is suggested on the base of hierarchy of concepts. An inherent slot of every node of the proposed ontology is a low level features vector (LLFV), which facilitates the search through the ontology. Part of the LLFV is the information about the object's parts. To partition an object a new approach is presented capable of defining the objects concavities used to mark the end points of weapon parts, considered as convexities. Further an existing matching approach is optimized to determine whether an ontological object matches the objects from an input image. Objects from derived ontological clusters will be considered for the matching process. Image resizing is studied and applied to decrease the runtime of the matching approach and investigate its rotational and scaling invariance. Set of experiments are preformed to validate the theoretical concepts.
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Nikolay Metodiev Sirakov, Sang Suh, Salvatore Attardo, "Integration of low level and ontology derived features for automatic weapon recognition and identification", Proc. SPIE 8049, Automatic Target Recognition XXI, 80490X (19 May 2011); doi: 10.1117/12.883664; https://doi.org/10.1117/12.883664

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