Titlebook: Intensive Care Medicine; Annual Update 2008 Jean-Louis Vincent (Head) Conference proceedings 2008 Springer-Verlag New York 2008 Internal me

回憶錄

書目名稱Intensive Care Medicine影響因子(影響力)




書目名稱Intensive Care Medicine影響因子(影響力)學(xué)科排名




書目名稱Intensive Care Medicine網(wǎng)絡(luò)公開度




書目名稱Intensive Care Medicine網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Intensive Care Medicine被引頻次




書目名稱Intensive Care Medicine被引頻次學(xué)科排名




書目名稱Intensive Care Medicine年度引用




書目名稱Intensive Care Medicine年度引用學(xué)科排名




書目名稱Intensive Care Medicine讀者反饋




書目名稱Intensive Care Medicine讀者反饋學(xué)科排名

挫敗

正面

C. Kirwan,I. MacPhee,B. Philipsd gives different weights to different orders simultaneously. Experimental results on two benchmark face databases demonstrate the effectiveness of the proposed method and indicate that our method is better than Fourier-PCA and LFMP, as well as other popular face recognition methods such as Gabor-ba

MELON

R. Cartin-Ceba,M. N. Gong,O. Gajicct as weights between stereo pairs to simulate the binocular perception properties. Therefore, we introduce the difference of binocular energy as part of the features. The depth/disparity information between stereo pairs contains much properties of stereoscopic vision, and we extract features from d

粗鄙的人

F. B. Mayr,S. Yende,D. C. Angusxperts and nonexperts. Furthermore, the existing 10 saliency models are tested on PMIDE, and the performance of these models are all unacceptable with AUC, CC, NSS and SAUC below 0.73, 0.47, 0.78 and 0.52, respectively. This study indicates that the saliency models specific to pathological microscop

量被毀壞

xperts and nonexperts. Furthermore, the existing 10 saliency models are tested on PMIDE, and the performance of these models are all unacceptable with AUC, CC, NSS and SAUC below 0.73, 0.47, 0.78 and 0.52, respectively. This study indicates that the saliency models specific to pathological microscop

整體

Allodynia

Valves

牽連

B. Lamia,M. R. Pinskyt 1D fully connected graph, in which each node represents a patch in the collected group. Finally, a collaborative filtering based on 3D graph Fourier transform (GFT) is conducted on the 3D patch group. We conduct shrinkage of the transform coefficients to get the denoised patches. Experimental resu