Titlebook: Combinatorial Image Analysis; 11th International W Ralf Reulke,Ulrich Eckardt,Konrad Polthier Conference proceedings 2006 Springer-Verlag B

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書(shū)目名稱Combinatorial Image Analysis影響因子(影響力)




書(shū)目名稱Combinatorial Image Analysis影響因子(影響力)學(xué)科排名




書(shū)目名稱Combinatorial Image Analysis網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Combinatorial Image Analysis網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Combinatorial Image Analysis被引頻次




書(shū)目名稱Combinatorial Image Analysis被引頻次學(xué)科排名




書(shū)目名稱Combinatorial Image Analysis年度引用




書(shū)目名稱Combinatorial Image Analysis年度引用學(xué)科排名




書(shū)目名稱Combinatorial Image Analysis讀者反饋




書(shū)目名稱Combinatorial Image Analysis讀者反饋學(xué)科排名

URN

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獸群

Branch Voxels and Junctions in 3D Skeletonsan space. This paper applies the concept of branch points in the 3D orthogonal grid, motivated by the need to analyze curve-like structures in digital images. These curve-like structures have been derived as 3D skeletons (by means of thinning). This paper discusses approaches of defining branch indi

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Matching of the Multi-channel Images with Improved Nonparametric Transformations and Weighted Binaryits many desirable properties at relatively small computation complexity: It concentrates on capturing mutual relation among pixels in a small neighbourhoods rather than bare intensity values, thus improving matching discrimination. It is also more resistive against noise and uneven lighting conditi

APNEA

Ventricle

A New Sub-pixel Map for Image Analysisd to pixel accuracy. This leads to limitations in the representation of complex structures (such as junctions, triangulations, and skeletons) and discards the sub-pixel information available in grayvalue and color images. We extend the planar map formalism to sub-pixel accuracy and introduce various

Affection

Extended Mumford-Shah Regularization in Bayesian Estimation for Blind Image Deconvolution and Segmenoisy images or video sequences. The Mumford-Shah functional is extended to have cost terms for the estimation of blur kernels via a newly introduced prior solution space. This functional is minimized using Γ-convergence approximation in an embedded alternating minimization within Neumann conditions.