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Titlebook: Industrial Applications of Renewable Biomass Products; Past, Present and Fu Silvia Nair Goyanes,Norma Beatriz D’Accorso Book 2017 Springer

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11#
發(fā)表于 2025-3-23 10:30:38 | 只看該作者
Isabel Natalia Vega,María Isabel Hernándezor the peak value of the histogram of normalized stroke width, the text polarity is also determined. Finally, we propose a local region growing method for text extraction using SLIC-superpixel. Unlike current existing methods of computing stroke width, such as gray level jump on a horizontal scan li
12#
發(fā)表于 2025-3-23 17:42:07 | 只看該作者
13#
發(fā)表于 2025-3-23 21:54:00 | 只看該作者
ssified by trained boosted decision trees and labeled with semantic classes e.g. tree, pedestrian, car, etc. The proposed method is evaluated both quantitatively and qualitatively on a challenging fixed-position . (TLS) Velodyne data set and two . (MLS), Paris-rue-Madam and NAVTEQ True databases. Ro
14#
發(fā)表于 2025-3-24 01:37:20 | 只看該作者
15#
發(fā)表于 2025-3-24 05:19:38 | 只看該作者
M. P. Arrieta,M. A. Peltzer,J. López,L. Peponitep of the DBSR can be changed to simpler consistency check method. In this way the DBSR computational complexity is reduced by 26?% with 0.2?% dBR average bitrate reduction for coded views and 0.1?% average bitrate increase for synthesized views. We show that proposed scheme outperforms the anchor
16#
發(fā)表于 2025-3-24 09:50:40 | 只看該作者
Laura G. Ribba,Jonathan D. Cimadoro,Norma Beatriz D’Accorso,Silvia Nair Goyanes LL-SVM is verified to find a balance between accuracy and efficiency. And among all the features including pixel value intensity, Gabor, LBP and HOG features, we find that HOG features are the most appropriate features to detect smiling faces, which, combined with RBF kernel SVM, achieve an accurac
17#
發(fā)表于 2025-3-24 12:13:41 | 只看該作者
at help to discover visual attributes by clustering. However, the latent attribute structure in real life is multi-relational, e.g. two different sport cars in different poses vs. a sport car and a family car in the same pose - what attribute can dominate similarity? Instead of clustering, a network
18#
發(fā)表于 2025-3-24 18:21:52 | 只看該作者
19#
發(fā)表于 2025-3-24 21:02:44 | 只看該作者
20#
發(fā)表于 2025-3-25 01:40:58 | 只看該作者
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