Automatic Object Identification Using Visual Low Level Feature Extraction and Ontological Knowledge

The present work is a part of research study aiming to develop an algorithm and a software system capable of quick identification of weapons and relations between human and a weapon in a scene. Bridging the semantic gap between the low level knowledge extracted from an image and the high level semantics needed to negotiate the weapon domain ontology is connected to the features extraction algorithms.

Integration of Low Level and Ontology Derived Features For Automatic Weapon Recognition and Identification

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.

Weapon Ontology Annotation Using Boundary Describing Sequences

This paper presents an approach to identify a weapon from a single image using a weapon ontology. Ontological nodes selected by expert store convex hull(CH) sequences for their descendants, where as the ontological leafs are labeled with object boundary sequences. The latter are generated from object boundary vectices, while the CH sequences are generated from objects'CHs. The object's boundary and CH are extracted by an active contour model.

From Shape to Threat Exploiting the Convergence Between Visual and Conceptual Organization for Weapon Identification and Threat Assessment

This pape presents an approach to identify a weapon from a single image using a weapon ontology. Ontological nodes selected by expert store convex hull(CH) sequences for their descendants, where as the ontological leafs are labeled with object boundary sequences. The latter are generated from object boundary vectices, while the CH sequences are generated from objects'CHs. The object's boundary and CH are extracted by an active contour model.