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Titlebook: Computer Vision - ECCV 2008; 10th European Confer David Forsyth,Philip Torr,Andrew Zisserman Conference proceedings 2008 Springer-Verlag Be

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發(fā)表于 2025-3-21 16:26:14 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Computer Vision - ECCV 2008
副標(biāo)題10th European Confer
編輯David Forsyth,Philip Torr,Andrew Zisserman
視頻videohttp://file.papertrans.cn/235/234148/234148.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Computer Vision - ECCV 2008; 10th European Confer David Forsyth,Philip Torr,Andrew Zisserman Conference proceedings 2008 Springer-Verlag Be
描述Welcome to the 2008EuropeanConference onComputer Vision. These proce- ings are the result of a great deal of hard work by many people. To produce them, a total of 871 papers were reviewed. Forty were selected for oral pres- tation and 203 were selected for poster presentation, yielding acceptance rates of 4.6% for oral, 23.3% for poster, and 27.9% in total. Weappliedthreeprinciples.First,sincewehadastronggroupofAreaChairs, the ?nal decisions to accept or reject a paper rested with the Area Chair, who wouldbeinformedbyreviewsandcouldactonlyinconsensuswithanotherArea Chair. Second, we felt that authors were entitled to a summary that explained how the Area Chair reached a decision for a paper. Third, we were very careful to avoid con?icts of interest. Each paper was assigned to an Area Chair by the Program Chairs, and each Area Chair received a pool of about 25 papers. The Area Chairs then identi?ed and rankedappropriatereviewersfor eachpaper in their pool, and a constrained optimizationallocated three reviewers to each paper. We are very proud that every paper received at least three reviews. At this point, authors were able to respond to reviews. The Area Chairs then needed to reac
出版日期Conference proceedings 2008
關(guān)鍵詞Support Vector Machine; action recognition; aerial imagery; algorithms; classification; edge detection; mo
版次1
doihttps://doi.org/10.1007/978-3-540-88693-8
isbn_softcover978-3-540-88692-1
isbn_ebook978-3-540-88693-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2008
The information of publication is updating

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Image Segmentation by Branch-and-Mincut introducing a high-level prior such as a shape prior or a color-distribution prior into the segmentation process typically results in an energy that is much harder to optimize. The main contribution of the paper is a new global optimization framework for a wide class of such energies. The framework
板凳
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地板
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Light-Efficient Photography by (1) collecting a sequence of photos and (2) controlling the aperture, focus and exposure time of each photo individually, we can span the given depth of field in less total time than it takes to expose a single narrower-aperture photo. Using this as a starting point, we obtain two key results. F
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發(fā)表于 2025-3-22 10:23:22 | 只看該作者
Flexible Depth of Field Photographye-off between depth of field and signal-to-noise ratio (SNR). For a dark scene, the aperture of the lens must be opened up to maintain SNR, which causes the DOF to reduce. Also, today’s cameras have DOFs that correspond to a single slab that is perpendicular to the optical axis. In this paper, we pr
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發(fā)表于 2025-3-22 16:50:11 | 只看該作者
Priors for Large Photo Collections and What They Reveal about Camerasel priors for statistics of such large photo collections that are independent of these factors. We then propose that properties of these factors can be recovered by examining the deviation between these statistical priors and the statistics of a slice of the overall photo collection that holds one f
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發(fā)表于 2025-3-23 00:27:23 | 只看該作者
CenSurE: Center Surround Extremas for Realtime Feature Detection and Matchinga: stability (persistence across viewpoint change) and accuracy (consistent localization across viewpoint change). In addition to the now-standard SIFT, SURF, FAST, and Harris detectors, we introduce a suite of scale-invariant center-surround detectors (CenSurE) that outperform the other detectors,
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發(fā)表于 2025-3-23 01:44:30 | 只看該作者
Searching the World’s Herbaria: A System for Visual Identification of Plant Speciesnd, and the system extracts the leaf shape and matches it to the shape of leaves of known species. In a few seconds, the system displays the top matching species, along with textual descriptions and additional images. This system is currently in use by botanists at the Smithsonian Institution Nation
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