ODYSSEY: Software development life cycle ontology

With the omnipresence of softwares in our Society from Information Technology (IT) services to autonomous agents, their systematic and efficient development is crucial for software developers. Hence, in this paper, we present an approach to assist intelligent agents (IA), whatever human beings or artificial systems, in theirs task to develop and configure softwares. The proposed method is an ontological, developer-centred approach aiding a software developer in decision making and interoperable information sharing through the use of the ODYSSEY ontology we developed for the software development life cycle (SDLC) domain. This ODYSSEY ontology has been designed following the Enterprise Ontology (EO) methodology and coded in Descriptive Logic (DL). Its implementation in OWL has been evaluated for case studies, showing promising results

Speeded up gradient vector flow B-spline active contours for robust and real-time tracking

Segmentation and tracking methods have been widely explore. However, they are often computationally heavy or require constraining assumptions. We present in this paper a new system for real-time simultaneous segmentation and tracking, without any hypothesis on target appearance, image background or camera properties. The proposed approach (SUGVPB) is an active contour modeled with B-splines and which evolution process is using a speeded up gradient vector flow, characterized by a faster computation of the edge diffusion process. The synergy of these two powerful components enables precise, robust and real-time tracking of complete non-rigid mobile objects. Our method has been validated on synthetic as well as natural video sequences.Anglai

Non-rigid object tracker based on a robust combination of parametric active contour and point distribution model

Our study considers the development of a reliable tracker for non-rigid objects evolving on cluttered background in crowded scenes captured by moving cameras. For this purpose, we propose an original method that combines two approaches, respectively based on parametric active contours (PAC) and on point distribution model (PDM). The PAC tracker relies on an effective and efficient implementation of contour convergence mechanism to bring a smooth contour to the edges of the target in real-time. The PDM approach collects feature points in the region delineated by the PAC tracker to build and update a model of the target in term of a feature point distribution. Formally, when a novel frame is considered, its feature points are matched with the PDM model. The matching information is used to initialize the novel PAC, whose convergence identify the points that are relevant to update the PDM for the next frame. Hence, the two approaches complement each others. The a priori information provided by the PDM makes the system robust towards occlusions, while the deformation of the PAC increases its robustness towards target appearance changes. Simulations on real-word video sequences demonstrate the performance of our approach.Anglai

Multi-feature vector flow for active contour tracking

In order to achieve both fast tracking and accurate object extraction, we present in this paper an original real-time active contour method, incorporating different feature maps into a common and homogeneous framework, defined by the multi-feature vector flow (MFVF). The MFVF active contour approach does not require any target prior model, and enables precise tracking of mobile deformable objects. The use of the MFVF, resulting from multiple selected features, brings robustness into the system towards complex situations, while our computationally efficient implementation of the MFVF scheme reaches the required speed range for tracking process. The proposed method has been successfully tested on real-world video sequences.Anglai