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

Julienne

書目名稱Computer Vision - ECCV 2008影響因子(影響力)




書目名稱Computer Vision - ECCV 2008影響因子(影響力)學科排名




書目名稱Computer Vision - ECCV 2008網(wǎng)絡公開度




書目名稱Computer Vision - ECCV 2008網(wǎng)絡公開度學科排名




書目名稱Computer Vision - ECCV 2008被引頻次




書目名稱Computer Vision - ECCV 2008被引頻次學科排名




書目名稱Computer Vision - ECCV 2008年度引用




書目名稱Computer Vision - ECCV 2008年度引用學科排名




書目名稱Computer Vision - ECCV 2008讀者反饋




書目名稱Computer Vision - ECCV 2008讀者反饋學科排名

有角

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

后天習得

玩忽職守

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

synovial-joint

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

justify

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

justify

daredevil

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,

Synthesize

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

Acumen