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Preface; Conference Organization; Contents; Part I Image and Video Signal Processing; 1 The election of Spectrum bands in Hyper-spectral image classification; Abstract; 1 Introduction; 2 proposed approach modules; 3 process of spectrum bands election algorithm; 4 detail of spectrum bands election algorithm; 5 result of experiment; References; 2 Evaluating a Virtual Collaborative Environment for Interactive Distance Teaching and Learning: A Case Study; Abstract; Keywords; 1 Introduction; 2 Related Work; 3 The Open Wonderland; 4 Experimental Design; 5 Discussion; 6 Conclusion; 7 Acknowledgement.
AbstractKeywords; 1 Introduction; 2 Feature Representations for Photo-Realistic FaceImage; 2.1 PCA-Based Feature; 2.2 Animation Unit (AU) Parameter; 2.3 Modeling and Generation of Facial Features Using HMM; 2.4 Mapping from AU Parameters to Pixel Image Using DNN; 3 Experiments; 3.1 Database; 3.2 Relation between the amount of training data and the objectivequality; 3.3 Performance comparison between the conventional andproposed techniques; 4 Conclusions and future work; Acknowledgment; References; 5 Silhouette Imaging for Smart Fence Applications with ZigBee Sensors; Abstract; Keywords.
1 Introduction2 Smart Fence via ZigBee Sensors; 3 Silhouette Imaging Method; 3.1 Sensing Model; 3.2 Imaging Algorithm; 4 Results and Discussions; 4.1 Results; 4.2 Discussions; 5 Conclusion; References; 6 Gender Recognition Using Local Block Difference Pattern; Abstract; Keywords; 1 Introduction; 2 Background Review; 3 The Proposed Approach; 4 Experimental Results; 4.1 Influence of Block Number; 4.2 Comparison with Other Methods; 5 Conclusions; Acknowledgments; References; 7 DBN-based Classifcation of Spatial-spectral Hyperspectral Data; Abstract; Keywords; 1 Introduction.
2 Spatial and Spectral Information Based HyperspectralImage Classification2.1 Structure of DBN; 2.2 Principle of the Classification Method; 2.3 Neighborhood Information Stitching Method; 2.4 Spectral-Spatial Information Stitching Method; 3 Experiments Results and Analysis; 3.1 Data Set Description; 3.2 Experimental and Results Analysis; 4 Conclusion; References; 8 Using CNN to Classify Hyperspectral Data Based on Spatial-spectral Information; Abstract; Keywords; 1 Introduction; 2 CNN; 3 Hyperspectral Image Classification Based on CNN; 3.1 Flowchart.
AbstractKeywords; 1 Introduction; 2 Feature Representations for Photo-Realistic FaceImage; 2.1 PCA-Based Feature; 2.2 Animation Unit (AU) Parameter; 2.3 Modeling and Generation of Facial Features Using HMM; 2.4 Mapping from AU Parameters to Pixel Image Using DNN; 3 Experiments; 3.1 Database; 3.2 Relation between the amount of training data and the objectivequality; 3.3 Performance comparison between the conventional andproposed techniques; 4 Conclusions and future work; Acknowledgment; References; 5 Silhouette Imaging for Smart Fence Applications with ZigBee Sensors; Abstract; Keywords.
1 Introduction2 Smart Fence via ZigBee Sensors; 3 Silhouette Imaging Method; 3.1 Sensing Model; 3.2 Imaging Algorithm; 4 Results and Discussions; 4.1 Results; 4.2 Discussions; 5 Conclusion; References; 6 Gender Recognition Using Local Block Difference Pattern; Abstract; Keywords; 1 Introduction; 2 Background Review; 3 The Proposed Approach; 4 Experimental Results; 4.1 Influence of Block Number; 4.2 Comparison with Other Methods; 5 Conclusions; Acknowledgments; References; 7 DBN-based Classifcation of Spatial-spectral Hyperspectral Data; Abstract; Keywords; 1 Introduction.
2 Spatial and Spectral Information Based HyperspectralImage Classification2.1 Structure of DBN; 2.2 Principle of the Classification Method; 2.3 Neighborhood Information Stitching Method; 2.4 Spectral-Spatial Information Stitching Method; 3 Experiments Results and Analysis; 3.1 Data Set Description; 3.2 Experimental and Results Analysis; 4 Conclusion; References; 8 Using CNN to Classify Hyperspectral Data Based on Spatial-spectral Information; Abstract; Keywords; 1 Introduction; 2 CNN; 3 Hyperspectral Image Classification Based on CNN; 3.1 Flowchart.