Titlebook: Advances in Visual Computing; 10th International S George Bebis,Richard Boyle,Mark Carlson Conference proceedings 2014 Springer Internation

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書目名稱Advances in Visual Computing影響因子(影響力)




書目名稱Advances in Visual Computing影響因子(影響力)學科排名




書目名稱Advances in Visual Computing網(wǎng)絡公開度




書目名稱Advances in Visual Computing網(wǎng)絡公開度學科排名




書目名稱Advances in Visual Computing被引頻次




書目名稱Advances in Visual Computing被引頻次學科排名




書目名稱Advances in Visual Computing年度引用




書目名稱Advances in Visual Computing年度引用學科排名




書目名稱Advances in Visual Computing讀者反饋




書目名稱Advances in Visual Computing讀者反饋學科排名

disparage

Multimodal Non-Rigid Registration Methods Based on Demons Models and Local Uncertainty Quantificatioes the monomodal intensity restriction of fluid-like registration (FLR) models, such as Demons-based registration algorithms, by applying a mapping that relies on an intensity uncertainty quantification in a local neighbourhood, bringing the target and source images into a common domain where they a

四目在模仿

Principal Axes-Based Asymmetry Assessment Methodology for Skin Lesion Image Analysisof skins lesions is usually done through the ABCD rule (asymmetry, border, color and differential structures) that classifies the lesion as benign, suspicious or highly suspicious of Malignant Melanoma. A methodology to assess the asymmetry of a skin lesion image in relation to each axis of inertia,

ATP861

MOT

Spatio-temporal Level-Set Based Cell Segmentation in Time-Lapse Image Sequencess work our interest is focused on cell segmentation over a set of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We utilize a region-based approach to curve evolution based on the level-set formulation.

HALL

A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependenciesther words, only one-step back dependencies are modeled which is a rather unrealistic assumption in most applications. In this paper, we propose a method for postulating HMMs with approximately infinitely-long time-dependencies. Our approach considers the whole history of model states in the postula

Erythropoietin

鄙視讀作

One-Shot Learning of Sketch Categories with Co-regularized Sparse Codingn-trivial due to the iconic nature of sketches, signified by large variances in both appearance and structure when compared with photographs. Prior works often utilize off-the-shelf low-level features and assume the availability of a large training set, rendering them sensitive towards abstraction a

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