56 research outputs found

    Advanced content-based semantic scene analysis and information retrieval: the SCHEMA project

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    The aim of the SCHEMA Network of Excellence is to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. Relevant research areas include: content-based multimedia analysis and automatic annotation of semantic multimedia content, combined textual and multimedia information retrieval, semantic -web, MPEG-7 and MPEG-21 standards, user interfaces and human factors. In this paper, recent advances in content-based analysis, indexing and retrieval of digital media within the SCHEMA Network are presented. These advances will be integrated in the SCHEMA module-based, expandable reference system

    Immersive video conferencing architecture using game engine technology

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    This paper introduces the use of gaming technology for the creation of immersive video conferencing systems. The system integrates virtual meeting rooms with avatars and life video feeds, shared across different clients. Video analysis is used to create a sense of immersiveness by introducing aspects of the real world in the virtual environment. This architecture will ease and stimulate the development of immersive and intelligent telepresence systems

    Quadrilateral-based region segmentation for tracking

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    We propose a novel quadrilateral based region segmentation method that is favorable for object tracking. Instead of using groups of pixels or regular blocks, it uses groups of connected quadrilaterals to represent regions. The proposed method derives the vertices of each quadrilateral from the edge map using the concept of center of masses. By merging the quadrilaterals, regions can be represented. The proposed method offers better data reduction than pixelwise region representation and better boundary approximation than block-based segmentation methods. Experimental results show that it generates a more reasonable region map, which is more suitable for object tracking, and a smaller number of regions than the seeded region growing, K-means clustering, and constrained gravitational clustering methods. © 2002 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

    Region and object segmentation algorithms in the Qimera segmentation platform

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    In this paper we present the Qimera segmentation platform and describe the different approaches to segmentation that have been implemented in the system to date. Analysis techniques have been implemented for both region-based and object-based segmentation. The region-based segmentation algorithms include: a colour segmentation algorithm based on a modified Recursive Shortest Spanning Tree (RSST) approach, an implementation of a colour image segmentation algorithm based on the K-Means-with-Connectivity-Constraint (KMCC) algorithm and an approach based on the Expectation Maximization (EM) algorithm applied in a 6D colour/texture space. A semi-automatic approach to object segmentation that uses the modified RSST approach is outlined. An automatic object segmentation approach via snake propagation within a level-set framework is also described. Illustrative segmentation results are presented in all cases. Plans for future research within the Qimera project are also discussed

    АВТОМАТИЧЕСКОЕ РЕФЕРИРОВАНИЕ ВИДЕОИНФОРМАЦИИ: ОБЗОР

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    Анализируются существующие методы и системы для создания видеорефератов на основе сегментации и анализа видеообъектов, полученных при обработке последовательных сцен отдельного видеофайла. Исследуются метрики и свойства, использующиеся при автоматическом и полуавтоматическом анализе видеопоследовательностей. Рассматриваются имеющиеся системы по распознаванию различных семантических объектов, находящихся в кадре на момент обработки. Изучаются предложенные методы для индексации и поиска видеоинформации

    Vision-Based 2D and 3D Human Activity Recognition

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    Deliverable D1.1 State of the art and requirements analysis for hypervideo

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    This deliverable presents a state-of-art and requirements analysis report for hypervideo authored as part of the WP1 of the LinkedTV project. Initially, we present some use-case (viewers) scenarios in the LinkedTV project and through the analysis of the distinctive needs and demands of each scenario we point out the technical requirements from a user-side perspective. Subsequently we study methods for the automatic and semi-automatic decomposition of the audiovisual content in order to effectively support the annotation process. Considering that the multimedia content comprises of different types of information, i.e., visual, textual and audio, we report various methods for the analysis of these three different streams. Finally we present various annotation tools which could integrate the developed analysis results so as to effectively support users (video producers) in the semi-automatic linking of hypervideo content, and based on them we report on the initial progress in building the LinkedTV annotation tool. For each one of the different classes of techniques being discussed in the deliverable we present the evaluation results from the application of one such method of the literature to a dataset well-suited to the needs of the LinkedTV project, and we indicate the future technical requirements that should be addressed in order to achieve higher levels of performance (e.g., in terms of accuracy and time-efficiency), as necessary

    Enhancing the Potential of the Conventional Gaussian Mixture Model for Segmentation: from Images to Videos

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    Segmentation in images and videos has continuously played an important role in image processing, pattern recognition and machine vision. Despite having been studied for over three decades, the problem of segmentation remains challenging yet appealing due to its ill-posed nature. Maintaining spatial coherence, particularly at object boundaries, remains difficult for image segmentation. Extending to videos, maintaining spatial and temporal coherence, even partially, proves computationally burdensome for recent methods. Finally, connecting these two, foreground segmentation, also known as background suppression, suffers from noisy or dynamic backgrounds, slow foregrounds and illumination variations, to name a few. This dissertation focuses more on probabilistic model based segmentation, primarily due to its applicability in images as well as videos, its past success and mainly because it can be enhanced by incorporating spatial and temporal cues. The first part of the dissertation focuses on enhancing conventional GMM for image segmentation using Bilateral filter due to its power of spatial smoothing while preserving object boundaries. Quantitative and qualitative evaluations are done to show the improvements over a number of recent approaches. The later part of the dissertation concentrates on enhancing GMM towards foreground segmentation as a connection between image and video segmentation. First, we propose an efficient way to include multiresolution features in GMM. This novel procedure implicitly incorporates spatial information to improve foreground segmentation by suppressing noisy backgrounds. The procedure is shown with Wavelets, and gradually extended to propose a generic framework to include other multiresolution decompositions. Second, we propose a more accurate foreground segmentation method by enhancing GMM with the use of Adaptive Support Weights and Histogram of Gradients. Extensive analyses, quantitative and qualitative experiments are presented to demonstrate their performances as comparable to other state-of-the-art methods. The final part of the dissertation proposes the novel application of GMM towards spatio-temporal video segmentation connecting spatial segmentation for images and temporal segmentation to extract foreground. The proposed approach has a simple architecture and requires a low amount of memory for processing. The analysis section demonstrates the architectural efficiency over other methods while quantitative and qualitative experiments are carried out to show the competitive performance of the proposed method
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