Titlebook: Computer Vision - ECCV 2004; 8th European Confere Tomás Pajdla,Ji?í Matas Conference proceedings 2004 Springer-Verlag Berlin Heidelberg 200

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書(shū)目名稱Computer Vision - ECCV 2004影響因子(影響力)




書(shū)目名稱Computer Vision - ECCV 2004影響因子(影響力)學(xué)科排名




書(shū)目名稱Computer Vision - ECCV 2004網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Computer Vision - ECCV 2004網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Computer Vision - ECCV 2004被引頻次




書(shū)目名稱Computer Vision - ECCV 2004被引頻次學(xué)科排名




書(shū)目名稱Computer Vision - ECCV 2004年度引用




書(shū)目名稱Computer Vision - ECCV 2004年度引用學(xué)科排名




書(shū)目名稱Computer Vision - ECCV 2004讀者反饋




書(shū)目名稱Computer Vision - ECCV 2004讀者反饋學(xué)科排名

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Learning and Bayesian Shape Extraction for Object Recognitionh taken is Bayesian: we adopt a region-based model that incorporates prior knowledge of specific shapes of interest. To quantify this prior knowledge, we address the problem of learning probability models for collections of observed shapes. Our method is based on the geometric representation and alg

簡(jiǎn)略

Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentationropriate cost functional with respect to both a level set function and a (vector-valued) labeling function, we jointly generate a segmentation (by the level set function) and a recognition-driven partition of the image domain (by the labeling function) which indicates where to enforce certain shape

類型

Integral Invariant Signaturesgroup, obtained by performing integral operations. While such integral invariants enjoy some of the desirable properties of their differential cousins, such as locality of computation (which allows matching under occlusions) and uniqueness of representation (in the limit), they are not as sensitive

MELD

Detecting Keypoints with Stable Position, Orientation, and Scale under Illumination Changesnown as ‘keypoints’ or ‘points of interest’. Their performance depends critically on the accuracy and reliability with which corresponding keypoints can be found in subsequent images. Among the many existing keypoint selection criteria, the popular F?rstner-Harris approach explicitly targets geometr

兇猛

Spectral Simplification of Graphshed into smaller subgraphs. If this is done, then the process may cast into a hierarchical framework and hence rendered suitable for parallel computation. In this paper we describe a spectral method which can be used to partition graphs into non-overlapping subgraphs. In particular, we demonstrate h