24 research outputs found

    A framework for event detection in field-sports video broadcasts based on SVM generated audio-visual feature model. Case-study: soccer video

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    In this paper we propose a novel audio-visual feature-based framework, for event detection in field sports broadcast video. The system is evaluated via a case-study involving MPEG encoded soccer video. Specifically, the evidence gathered by various feature detectors is combined by means of a learning algorithm (a support vector machine), which infers the occurrence of an event, based on a model generated during a training phase, utilizing a corpus of 25 hours of content. The system is evaluated using 25 hours of separate test content. Following an evaluation of results obtained, it is shown for this case, that both high precision and recall statistics are achievable

    Event detection based on generic characteristics of field-sports

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    In this paper, we propose a generic framework for event detection in broadcast video of multiple different field-sports. Features indicating significant events are selected, and robust detectors built. These features are rooted in generic characteristics common to all genres of field-sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested across multiple genres of field-sports including soccer, rugby, hockey and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable

    3D Scene Annotation for Efficient Rendering on Mobile Devices

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    This paper presents a new approach for efficient 3D rendering on mobile devices, where selective rendering can be achieved with the help of 3D scene annotation. By taking advantage of first person environments in most 3D applications, we are able to annotate the flooring details of the 3D space. This allows 3D environments to be interfaced using a higher level view of objects. With the higher level of scene understanding, it is possible to determine which 3D objects are not required for loading or rendering based on the viewer’s location and its surrounding constraints

    Similarity measures for mid-surface quality evaluation

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    Mid-surface models are widely used in engineering analysis to simplify the analysis of thin-walled parts, but it can be difficult to ensure that the mid-surface model is representative of the solid part from which it was generated. This paper proposes two similarity measures that can be used to evaluate the quality of a mid-surface model by comparing it to a solid model of the same part. Two similarity measures are proposed; firstly a geometric similarity evaluation technique based on the Hausdorff distance and secondly a topological similarity evaluation method which uses geometry graph attributes as the basis for comparison. Both measures are able to provide local and global similarity evaluation for the models. The proposed methods have been implemented in a software demonstrator and tested on a selection of representative models. They have been found to be effective for identifying geometric and topological errors in mid-surface models and are applicable to a wide range of practical thin-walled designs

    Adaptive face modelling for reconstructing 3D face shapes from single 2D images

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    Example-based statistical face models using principle component analysis (PCA) have been widely deployed for three-dimensional (3D) face reconstruction and face recognition. The two common factors that are generally concerned with such models are the size of the training dataset and the selection of different examples in the training set. The representational power (RP) of an example-based model is its capability to depict a new 3D face for a given 2D face image. The RP of the model can be increased by correspondingly increasing the number of training samples. In this contribution, a novel approach is proposed to increase the RP of the 3D face reconstruction model by deforming a set of examples in the training dataset. A PCA-based 3D face model is adapted for each new near frontal input face image to reconstruct the 3D face shape. Further an extended Tikhonov regularisation method has been

    [[alternative]]Distance Learning Courseware Design Using Influence Diagram

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    計畫編號:NSC91-2520-S032-011研究期間:200208~200307研究經費:548,000[[sponsorship]]行政院國家科學委員

    Building and Tracking Hierarchical Geographical & Temporal Partitions for Image Collection Management on Mobile Devices

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    International audienceUsage of mobile devices (phones, digital cameras) raises the need for organizing large personal image collections. In accordance with studies on user needs, we propose a statistical criterion and an associated optimization technique, relying on geo-temporal image metadata, for building and tracking a hierarchical structure on the image collection. In a mixture model framework, particularities of the application and typical data sets are taken into account in the design of the scheme (incrementality, ability to cope with non-Gaussian data, with both small and large samples). Results are reported on real data sets

    3D Content-Based Retrieval in Artwork Databases

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    International audienceIn this paper, we present first results obtained in the frame of the EROS-3D project, which aims at dealing with a collection of artwork 3D models, i.e. visualize them, classify them and compare them. Some 3D descriptors are used, in association with our active learning search engine RETIN. 3D features are described as well as our new system of classification and retrieval of objects, which we called RETIN-3D

    Scaffolding for activity supervision and self-regulation in virtual university

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    [[abstract]]Distance education has been an important research issue of multimedia computing and communication. Since the instructional activities are implemented on cyberspace, how to control behaviors of students and to increase the degree of communication awareness has been a challenging issue. This paper presents an advanced Petri Net model to analyze the workflow of a web-based multiple participants virtual environment. The presented approach not only can conspicuously help the developer to comprehend the interaction relationship between the client-server virtual environments but also to easily construct a shared virtual world.We proposed a system based on the scaffolding theory. Behaviors of students are supervised by an intelligent control system, which is programmed by the instructor under our generic interface. The interface is built based on virtual reality and real-time communication technologies. Students and instructors have their individual avatars that are controlled by a video game like navigation. Those behaviors that violate virtual campus regulations are detected and interceptive actions are performed. Problems of providing the multi-user interaction on the Web and the solutions proposed by the Petri Net model are fully elaborated here. This paper can be used as a basic/fundamental research framework and tools to study and understand the characteristics of e-learning and to explore its optimal education application.[[notice]]補正完畢[[incitationindex]]E
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