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生理机能中的深度学习(英文版) 版权信息
- ISBN：9787030705730
- 条形码：9787030705730 ; 978-7-03-070573-0
- 装帧：一般胶版纸
- 册数：暂无
- 重量：暂无
- 所属分类：>
生理机能中的深度学习(英文版) 内容简介
随着年龄的增长，生理机能的下降，如果不采取必要的措施，会导致高血压、心脏病等一些疾病的并发症。如果老年人能够进行生理评估，这些风险可以大大降低。近年来，由于预防对老年人的重要性，许多研究人员将注意力集中在对人体生理功能的评估上。我们写这本书是为了介绍我们认为是生理功能评估的关键技术，帮助读者更好地了解生理功能评估
生理机能中的深度学习(英文版) 目录
Preface
Chapter 1 Motion Detection with Skeleton Tracking
1.1 Introduction
1.1.1 Fog Computing-based Methods for Physiological Function Assessment
1.1.2 Motion Detection with Skeleton Tracking for Physiological Function Assessment
1.2 Motion Detection Method
1.2.1 Physiological Function Assessment Based on Fog Computing
1.2.2 Motion Recognition with RGB-D Cameras
1.3 Validation for Motion Detection
1.3.1 Comparison with Digital Angle Protractor
1.3.2 Effect of Motion Detection
1.3.3 Effect of Gait Analysis
1.4 3D Stcreo Human Trajectory Learning and Prediction
1.4.1 Method Overview
1.4.2 Twin Deep Neural Networks with Stereo Constraint for 3D Human Pose Estimation
1.4.3 Constructing the Forward and Backward Prediction Networks
1.5 Validation for 3D Pedestrian Trajectory Prediction
1.5.1 3D Pedestrian Trajectory Dataset
1.5.2 Evaluation Metrics and Protocol
1.5.3 Performance Evaluations and Comparison
1.6 Conclusions
References
Chapter 2 Facial Expression and Emotion Recognition
2.1 Introduction
2.2 Methodologies
2.2.1 Patch Manifold
2.2.2 Patch Discriminative Analysis
2.2.3 Optimization
2.3 Patch Discriminative Analysis Network
2.4 Facial Expression Recognition Validation
2.5 Methodologies Based on LMDAP
2.5.1 Discriminant Embedding Space
2.5.2 Convolutional Kernel Learning Based on the Proposed LMDAP Algorithm
2.5.3 Local Manifold Discriminant Analysis Projections Network
2.6 Validation for Method Based on LMDAP
2.6.1 Datasets
2.6.2 Parameter Setting
2.6.3 Face Recognition Experiments on Extended Yale B Dataset
2.6.4 Face Recognition Experiments on AR Dataset
2.6.5 Face Recognition Experiments on FERET Dataset
2.6.6 Face Verification: YTF
2.6.7 Impact of Parameters
2.6.8 Impact of the Number Block Size
2.6.9 Impact of the Block Overlap Ratio
2.7 Emotion Recognition Based Acoustic Features
2.7.1 MFCC Related Features Extraction
2.7.2 Speech Emotion Recognition Based on CNN-LSTM
2.7.3 Attention-based CNN-BiLSTM Recognition Model (CNN AttBiLSTM)
2.8 Validation for Speech Emotion Recognition
2.8.1 Datasets
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