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Titlebook: Computer Vision - ECCV 2000; 6th European Confere David Vernon Conference proceedings 2000 Springer-Verlag Berlin Heidelberg 2000 3-D visio

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發(fā)表于 2025-3-21 16:28:16 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computer Vision - ECCV 2000
副標(biāo)題6th European Confere
編輯David Vernon
視頻videohttp://file.papertrans.cn/235/234004/234004.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision - ECCV 2000; 6th European Confere David Vernon Conference proceedings 2000 Springer-Verlag Berlin Heidelberg 2000 3-D visio
描述Ten years ago, the inaugural European Conference on Computer Vision was held in Antibes, France. Since then, ECCV has been held biennially under the auspices of the European Vision Society at venues around Europe. This year, the privilege of organizing ECCV 2000 falls to Ireland and it is a signal honour for us to host what has become one of the most important events in the calendar of the computer vision community. ECCV is a single-track conference comprising the highest quality, previously unpublished, contributed papers on new and original research in computer vision. This year, 266 papers were submitted and, following a rigorous double-blind review process, with each paper being reviewed by three referees, 116 papers were selected by the Programme Committee for presentation at the conference. The venue for ECCV 2000 is the University of Dublin, Trinity College. - unded in 1592, it is Ireland’s oldest university and has a proud tradition of scholarship in the Arts, Humanities, and Sciences, alike. The Trinity campus, set in the heart of Dublin, is an oasis of tranquility and its beautiful squares, elegant buildings, and tree-lined playing- elds provide the perfect setting for an
出版日期Conference proceedings 2000
關(guān)鍵詞3-D vision; Stereo; computer vision; image processing; object recognition; pattern recognition; robot; robo
版次1
doihttps://doi.org/10.1007/3-540-45054-8
isbn_softcover978-3-540-67685-0
isbn_ebook978-3-540-45054-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2000
The information of publication is updating

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Unsupervised Learning of Models for Recognitions on a particular type of model where objects are represented as flexible constellations of rigid parts (features). The variability within a class is represented by a joint probability density function (pdf) on the shape of the constellation and the output of part detectors. In a first stage, the me
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Learning over Multiple Temporal Scales in Image Databases the integration of information from successive queries assuring faster convergence to the desired target images. Over long time scales (retrieval sessions) it allows the retrieval system to tailor itself to the preferences of particular users. We address the issue of learning by formulating retriev
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Colour Image Retrieval and Object Recognition Using the Multimodal Neighbourhood Signatureresented by colour-based features computed from image neighbourhoods with multi-modal colour density function. Stable invariants are derived from modes of the density function that are robustly located by the mean shift algorithm. The problem of extracting local invariant colour features is addresse
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Approximate N-View Stereoare increasingly tighter supersets of an unknown, arbitrarily-shaped 3D scene. By studying 3D shapes that reproduce the input photographs up to a special image transformation called a ., we prove that (1) these shapes can be organized hierarchically into nested supersets of the scene, and (2) they c
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A Minimal Set of Constraints for the Trifocal Tensor eight nonlinear algebraic constraints are enough. This result is in accordance with the theoretically expected number of eight independent constraints and novel since the to date known sets of sufficient constraints contain at least 12 conditions. Up to now, research and formulation of constraints
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Combining Elastic and Statistical Models of Appearance Variation elastic or optical-flow-based methods, in which deformation is described by many local parameters together with some regularisation constraints. We use an Active Appearance Model (AAM) as the global model, which can match a statistical model of appearance to a new image rapidly. However, the amount
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