Showing papers by "Shyamanta M. Hazarika published in 2007"

Enhanced Shape Context for Object Recognition

Abstract: This paper presents an enhanced approach to recognize objects based on a similarity measure obtained from shape context. Typically, shape context computation samples at regular interval on the contour of an object without regard to landmarks. Corner points of an object being landmarks on the contour; set of corner points is a good descriptor of shape. The paper explores the possibility of computing shape context by sampling the corner points using arch height. Sampling on this boundary feature of the object considerably reduces the number of points. Landmark based shape context provides a stricter algorithm on similarity. Shape context based object recognition being an iterative process involving comparisons and transformations, the reduction in the number of sample points provides a basis for faster recognition of similar objects.

Qualitative Self-Localization using a Spatio-Temporal Ontology: A Preliminary Report

Abstract: Self-localization the correlation of current and former impressions of the world is an essential ability for most mobile agents. In an inhabited dynamic system, high precision metric approaches demand accurate sensor devices and large computational power. In contrast, during the last few years qualitative methods have been explored to overcome mainly problems regarding complexity and stability. This paper describes a qualitative approach to self-localization. With space-time as the ontological primitive and using distinct spatiotemporal patterns for categories of objects, selflocalization within an integrated spatio-temporal framework is investigated. Given a record of local surveys, we show how space-time history descriptions might be abduced from local surveys and spatio-temporal patterns. The qualitative mereotopological world model so constructed forms a basis for self-localization. keywords: spatio-temporal reasoning, selflocalization, space-time.

Pattern Mining as Abduction from Snapshots to Spatio-temporal Patterns

Abstract: The paper describes an abductive framework for mining spatio-temporal patterns. The spatial representation language RCC-8, often referred to as Region Connection Calculus and its spatio-temporal extension, ST0, a fragment of Prepositional Spatio-temporal Logic is used as the knowledge representation formalism. Abductive reasoning may yield more than one possible answer and is accompanied by some preference criteria. Here, the abduction technique is circumscription which implements the heuristic that changes should only occur when forced to.

Pattern Mining as Abduction: From Snapshots to Spatio-Temporal Sequential Patterns

Abstract: The focus of this paper is towards development of a logical framework for mining spatio-temporal sequential patterns. The spatial representation language RCC-8, often referred to as region connection calculus and its spatio-temporal extension, ST0, a fragment of prepositional spatio-temporal logic is used as the knowledge representation formalism. Standard abduction is used for mining sequential patterns in spatio-temporal data. Abductive reasoning may yield more than one possible answer and is accompanied by some preference criteria expressed using heuristics. Here, the abduction technique is circumscription which implements the heuristic that changes should only occur when forced to. Various additional heuristics to drive the selection of preferred explanations are discussed.

Qualitative Framework for Vision-based Closure Grasps

Abstract: Vision based grasping formalisms require complete quantitative knowledge and are often susceptible to slight errors in shape and position of the object. Driven by the fact that everyday spatial reasoning is through qualitative abstractions, the focus of this paper is towards development of a qualitative framework for synthesis of closure grasps. The spatial representation language RCC-8, often referred to as Region Connection Calculus and its spatio-temporal extension, ST0, a tractable fragment of Propositional Spatio-temporal Logic is used as the knowledge representation formalism. Qualitative grasping schemes within this framework are explored and set of algorithms for synthesizing closure grasps is presented.