Interaction between high-level and low-level image analysis for semantic video object extraction

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Fully Automated Texture Tracking Based on Natural Features Extraction and Template Matching

ACE 134In this work we propose a novel approach to real-time texture tracking and registration, based on natural feature extraction from planar objects and template matching, Our method is oriented to planar objects with arbitrary textures but with rectangular topologies and well contrasted contours and does not require any external fiducial marker, either for the set-up or the tracking phases. Once the initial pose condition is obtained, previous planar object information is used to compute subsequent planar object’s pose, so that the time coherence of the input video stream is exploited. Our system is completely automated and produces real-time efficient tracking which can be applied to entertainment AR applications and other. The paper discusses also the novelty of the approach, in relation to other existing texture tracking algorithms.ADETTI/ISCT

Recovering Architectural Variability of a Family of Product Variants

A Software Product Line (SPL) aims at applying a pre-planned systematic reuse of large-grained software artifacts to increase the software productivity and reduce the development cost. The idea of SPL is to analyze the business domain of a family of products to identify the common and the variable parts between the products. However, it is common for companies to develop, in an ad-hoc manner (e.g. clone and own), a set of products that share common functionalities and differ in terms of others. Thus, many recent research contributions are proposed to re-engineer existing product variants to a SPL. Nevertheless, these contributions are mostly focused on managing the variability at the requirement level. Very few contributions address the variability at the architectural level despite its major importance. Starting from this observation, we propose, in this paper, an approach to reverse engineer the architecture of a set of product variants. Our goal is to identify the variability and dependencies among architectural-element variants at the architectural level. Our work relies on Formal Concept Analysis (FCA) to analyze the variability. To validate the proposed approach, we experimented on two families of open-source product variants; Mobile Media and Health Watcher. The results show that our approach is able to identify the architectural variability and the dependencies