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Titlebook: Image and Video Technology; PSIVT 2017 Internati Shin‘ichi Satoh Conference proceedings 2018 Springer International Publishing AG, part of

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41#
發(fā)表于 2025-3-28 16:50:13 | 只看該作者
42#
發(fā)表于 2025-3-28 20:53:53 | 只看該作者
43#
發(fā)表于 2025-3-29 02:44:57 | 只看該作者
Chuanyuan Lao,Qingtang Liu,Linjing Wu,Jingxiu Huang,Gang Zhao control of bone remodeling. The discovery that the receptor activator of NF-κB (ligand) RANKL/RANK system plays a pivotal role in both adaptive immunity and osteoclastogenesis has provided molecular evidence firmly linking the immune system and bone. Although studies from our laboratory and from ot
44#
發(fā)表于 2025-3-29 03:24:20 | 只看該作者
Xin Li,Han Lyu,Jiehan Zhou,Shuai Cao,Xin Liu control of bone remodeling. The discovery that the receptor activator of NF-κB (ligand) RANKL/RANK system plays a pivotal role in both adaptive immunity and osteoclastogenesis has provided molecular evidence firmly linking the immune system and bone. Although studies from our laboratory and from ot
45#
發(fā)表于 2025-3-29 08:53:52 | 只看該作者
Deep Transfer Feature Based Convolutional Neural Forests for?Head Pose Estimationng. In this paper, a novel deep transfer feature based on convolutional neural forest method (D-CNF) is proposed for head pose estimation. Deep transfer features are extracted from facial patches by a transfer network model, firstly. Then, a D-CNF is devised to integrate random trees with the repres
46#
發(fā)表于 2025-3-29 15:03:29 | 只看該作者
47#
發(fā)表于 2025-3-29 18:47:43 | 只看該作者
48#
發(fā)表于 2025-3-29 23:04:30 | 只看該作者
Facial Expression Recognition Using Cascaded Random Forest Based on Local Featuresaction. Researches in this field have made great progress. However, continuous efforts should be made to further improve the recognition accuracy for practical use. In this paper, an effective method is proposed for FER using a cascaded random forest based on local features. First, the hybrid featur
49#
發(fā)表于 2025-3-30 00:12:20 | 只看該作者
50#
發(fā)表于 2025-3-30 05:27:00 | 只看該作者
Selecting Salient Features from Facial Components for Face Recognitioncision when it selects distinctive and salient features from the feature space. This work proposes an approach to select salient features from facial components for identification and verification, disregard of the face configuration. The proposed method employs two local feature descriptors, Scale
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