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Biometric Recognition [[electronic resource] ] : 11th Chinese Conference, CCBR 2016, Chengdu, China, October 14-16, 2016, Proceedings / / edited by Zhisheng You, Jie Zhou, Yunhong Wang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng, Qijun Zhao
Biometric Recognition [[electronic resource] ] : 11th Chinese Conference, CCBR 2016, Chengdu, China, October 14-16, 2016, Proceedings / / edited by Zhisheng You, Jie Zhou, Yunhong Wang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng, Qijun Zhao
Edizione [1st ed. 2016.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
Descrizione fisica 1 online resource (XVII, 778 p. 358 illus.)
Disciplina 006
Collana Image Processing, Computer Vision, Pattern Recognition, and Graphics
Soggetto topico Biometrics (Biology)
Pattern recognition
Optical data processing
Algorithms
Computer graphics
Application software
Biometrics
Pattern Recognition
Image Processing and Computer Vision
Algorithm Analysis and Problem Complexity
Computer Graphics
Information Systems Applications (incl. Internet)
ISBN 3-319-46654-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Face Recognition and Analysis -- Fingerprint, Palm-print and Vascular Biometrics -- Iris and Ocular Biometrics -- Behavioral Biometrics -- Affective Computing -- Feature Extraction and Classification Theory -- Anti-Spoofing and Privacy -- Surveillance -- DNA and Emerging Biometrics.
Record Nr. UNISA-996465405303316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Biometric Recognition : 11th Chinese Conference, CCBR 2016, Chengdu, China, October 14-16, 2016, Proceedings / / edited by Zhisheng You, Jie Zhou, Yunhong Wang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng, Qijun Zhao
Biometric Recognition : 11th Chinese Conference, CCBR 2016, Chengdu, China, October 14-16, 2016, Proceedings / / edited by Zhisheng You, Jie Zhou, Yunhong Wang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng, Qijun Zhao
Edizione [1st ed. 2016.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
Descrizione fisica 1 online resource (XVII, 778 p. 358 illus.)
Disciplina 006
Collana Image Processing, Computer Vision, Pattern Recognition, and Graphics
Soggetto topico Biometrics (Biology)
Pattern recognition
Optical data processing
Algorithms
Computer graphics
Application software
Biometrics
Pattern Recognition
Image Processing and Computer Vision
Algorithm Analysis and Problem Complexity
Computer Graphics
Information Systems Applications (incl. Internet)
ISBN 3-319-46654-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Face Recognition and Analysis -- Fingerprint, Palm-print and Vascular Biometrics -- Iris and Ocular Biometrics -- Behavioral Biometrics -- Affective Computing -- Feature Extraction and Classification Theory -- Anti-Spoofing and Privacy -- Surveillance -- DNA and Emerging Biometrics.
Record Nr. UNINA-9910484414403321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Biometric Recognition [[electronic resource] ] : 10th Chinese Conference, CCBR 2015, Tianjin, China, November 13-15, 2015, Proceedings / / edited by Jinfeng Yang, Jucheng Yang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng
Biometric Recognition [[electronic resource] ] : 10th Chinese Conference, CCBR 2015, Tianjin, China, November 13-15, 2015, Proceedings / / edited by Jinfeng Yang, Jucheng Yang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng
Edizione [1st ed. 2015.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Descrizione fisica 1 online resource (XVIII, 739 p. 377 illus. in color.)
Disciplina 006
Collana Image Processing, Computer Vision, Pattern Recognition, and Graphics
Soggetto topico Biometrics (Biology)
Pattern recognition
Optical data processing
Algorithms
Computer graphics
Application software
Biometrics
Pattern Recognition
Image Processing and Computer Vision
Algorithm Analysis and Problem Complexity
Computer Graphics
Information Systems Applications (incl. Internet)
ISBN 3-319-25417-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- Face -- Adaptive Quotient Image with 3D Generic Elastic Models for Pose and Illumination Invariant Face Recognition -- 1 Introduction -- 2 Adaptive Quotient Image -- 2.1 Quotient Image -- 2.2 Adaptive Quotient Image -- 3 3D Face Reconstruction by GEM -- 4 Face Recognition via Pose-Specific Metric -- 4.1 Pose Estimation and Alignment -- 4.2 Recognition via Pose-Specific Metric -- 5 Experiments and Results -- 5.1 Results -- 6 Conclusion -- References -- Low Rank Analysis of Eye Image Sequence - A Novel Basis for Face Liveness Detection -- 1 Introduction -- 2 Motivations -- 3 Proposed Method -- 3.1 Sample Noising Model -- 3.2 Solutions of the Noising Model -- 3.3 Basis for Classification -- 3.4 The Proposed Algorithm -- 4 Experiments -- 5 Conclusion -- References -- Non-negative Compatible Kernel Construction for Face Recognition -- 1 Introduction -- 2 Nonnegative Compatible Kernel Construction -- 2.1 Symmetric NMF -- 2.2 Nonnegative Interpolatory Basis Function Construction -- 2.3 Nonnegative Compatible Kernel Construction -- 3 Experimental Results -- 3.1 Experiments on ORL Database -- 3.2 Experiments on Pain Expression Database -- 4 Conclusions -- References -- 3D Face Recognition Using Local Features Matching on Sphere Depth Representation -- 1 Introduction -- 1.1 Related Work -- 1.2 Motivation and Approach Overview -- 2 Generation of Sphere Depth Image -- 3 Local Feature Extraction on Sphere Depth Image -- 3.1 Problem in Keypoints Selection -- 3.2 Ranking Keypoints in Keypoints Selection -- 4 Experiment Analysis -- 4.1 Experiment on Ran nking Model -- 4.2 Experiment on Pose Change 3D Faces Images -- 5 Conclusion -- References -- Face Recognition Using Local PCA Filters -- 1 Introduction -- 2 Method -- 2.1 Filter Learning -- 2.2 Feature Coding -- 3 Experiment -- 3.1 Experiment on Feret Database.
3.2 Experiment on LFW Database -- 4 Conclusion -- References -- Block Statistical Features-based Face Verification on Second Generation Identity Card -- 1 Introduction -- 2 Face Representation Based on LGBP -- 3 The Proposed Algorithm -- 3.1 Face Presentation Based on BSF -- 3.2 Energy Check on Gabor Filter -- 3.3 Face Verification Based on BSF -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Experiment with NEU-ID Database -- 5 Conclusion and Discussion -- References -- Towards Practical Face Recognition: A Local Binary Pattern Non Frontal Faces Filtering Approach -- 1 Introduction -- 2 Overall Design Framework -- 2.1 Face Detection -- 2.2 LBP Feature Extraction -- 3 Experiment -- 3.1 Establish Facial Pose Database -- 3.2 Experimental Procedure and Results -- 4 Summary and Prospect -- References -- Metric Learning Based False Positives Filtering for Face Detection -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Experiments on Our Wild Dataset -- 4.3 Experiments on FDDB -- 5 Conclusion -- References -- Face Recognition via Compact Fisher Vector -- 1 Introduction -- 2 Fisher Vector and Related Encoding Strategies -- 2.1 Fisher Kernel and Fisher Vector -- 2.2 Fisher Vector Normalization -- 2.3 Integrating Spatial Information -- 2.4 VLAD and Intra-Normalization -- 3 Compact Fisher Vector Representation -- 3.1 Sparsifying Fisher Vector -- 3.2 Fisher Vector with First Order Statistically Only -- 3.3 Residual Normalization -- 3.4 Tweaking Fisher Vector Representation -- 3.5 Normalization -- 4 Experiments -- 4.1 FERET -- 4.2 Labeled Faces in the Wild (LFW) -- 5 Conclusion -- References -- Nonlinear Metric Learning with Deep Convolutional Neural Network for Face Verification -- 1 Introduction -- 2 Related Work -- 2.1 Similarity Distance Metric Learning.
2.2 Deep Learning and Convolutional Neural Network -- 3 Proposed Method -- 3.1 Nonlinear Metric Learning with Deep ConvNet -- 3.2 Discrimination Similarity Distance Metric with Deep ConvNets -- 3.3 Implementation Details -- 4 Preliminary Experiment -- 4.1 Datasets and Experimental Settings -- 4.2 Comparison with Existing Deep Metric Learning Methods -- 4.3 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Locally Collaborative Representation in SimilarSubspace for Face Recognition -- 1 Introduction -- 2 Sparse Representation and Collaborative Representation -- 2.1 Sparse Representation Based Classification (SRC) -- 2.2 Collaborative Representation Based Classification (CRC) -- 3 Locally Collaborative Representation Based Classification -- 4 Experimental Results -- 5 Conclusion and Discussion -- References -- A DCNN and SDM Based Face Alignment Algorithm -- 1 Introduction -- 2 Coarsely Localize 5 Landmarks Based on DCNN -- 3 Finely Localize 68 Landmarks Based on SDM -- 3.1 Initialization -- 3.2 Finetune Landmarks -- 4 Experiments and Analysis -- 5 Conclusion -- References -- Robust Face Detection Based on Enhanced Local Sensitive Support Vector Machine -- 1 Introduction -- 2 Background: LSSVM -- 2.1 Discussion -- 3 Proposed Method -- 3.1 The Adaboost Based Background Filter -- 3.2 Locality-Sensitive SVM Using Kernel Combination -- 4 Experiments -- 4.1 Evaluation on CMU+MIT Dataset -- 4.2 Evaluation on FDDB Dataset -- 5 Conclusions -- References -- An Efficient Non-negative Matrix Factorization with Its Application to Face Recognition -- 1 Introduction -- 2 Traditional NMF -- 3 The Proposed NMF -- 4 Experimental Results -- 4.1 Comparisons on Convergence -- 4.2 Comparisons on Performance -- 5 Conclusions -- References -- Patch-based Sparse Dictionary Representation for Face Recognition with Single Sample per Person.
1 Introduction -- 2 Related Work -- 3 Our Proposed Method -- 4 Classification -- 5 Experiment -- 6 Conclusion -- References -- Non-negative Sparsity Preserving Projections Algorithm Based Face Recognition -- 1 Introduction -- 2 Algorithm Overview -- 2.1 Locality Preserving Projections -- 2.2 Non-negative Sparsity Preserving Projections -- 3 Experiments -- 3.1 Experiments on RL OR Face Database -- 3.2 Experiments on FERET Face Database -- 3.3 Experimental Analysis -- 4 Conclusions -- References -- WLD-TOP Based Algorithm against Face Spoofing Attacks -- 1 Introduction -- 2 Related Work -- 3 WLD from Three Orthogonal Planes (WLD-TOP) for Image Representation -- 3.1 Modified WLD -- 3.2 WLD-TOP -- 4 Experiments -- 4.1 Data Set -- 4.2 Results on the Intra-database -- 4.3 Results on the Cross-Database -- 4.4 Effectiveness of Each WLD-TOP Plane -- 5 Conclusion -- References -- Heterogeneous Face Recognition Based on Super Resolution Reconstruction by Adaptive Multi-dictionary Learning -- 1 Introduction -- 2 Sketch-to-Photo Transformation -- 3 Super-Resolution of Synthesized Photos -- 3.1 Super Resolution Reconstruction Based on Sparse Representation -- 3.2 Adaptive Multi-dictionary Learning -- 3.3 Training Samples Clustering -- 3.4 Multi-dictionary Learning -- 3.5 Super Resolution Reconstruction Model -- 4 Face Recognition Based on 2DMFA -- 4.1 Marginal Fisher Analysis -- 4.2 Two-Dimensional Marginal Fisher Analysis -- 5 Experiments -- 6 Conclusion -- References -- 3D Face Recognition Fusing Spherical Depth Map and Spherical Texture Map -- 1 Introduction -- 2 Pure Face Extraction -- 3 Recognition Process -- 3.1 Spherical Depth Map and Spherical Texture Map -- 3.2 Sparse Representation -- 4 Experiments -- 4.1 Database -- 4.2 Recognition -- 5 Conclusion -- References -- Privacy Preserving Face Identification in the Cloud through Sparse Representation.
1 Introduction -- 2 Background -- 2.1 Cryptography Primitives -- 2.2 SCiFI Overview -- 3 Privacy Preserving Face Identification -- 3.1 Modified Sparse Representation Based Face Identification -- 3.2 Private Face Identification Protocol -- 4 Experimental Results -- 5 Conclusion and Discussion -- References -- Infrared Face Recognition Based on ODP of Local Binary Patterns -- 1 Introduction -- 2 Discriminative Patterns Based on Local Binary Patterns -- 3 Optimized Discriminative Patterns (ODP) of LBP -- 4 The Multi-classifier Based on Voting Mechanism -- 5 Experiment Results -- 6 Conclusions -- References -- Image Classification Based on Discriminative Dictionary Pair Learning -- 1 Introduction -- 2 Discriminative Dictionary Pair Learning -- 3 Optimization -- 4 Classification Scheme -- 5 Experiments -- 5.1 Face Recognition -- 5.2 Handwritten Digit Recognition -- 6 Conclusion -- References -- Weber Local Gradient Pattern (WLGP) Method for Face Recognition -- Introduction -- 2 Proposed Method -- 2.1 Weber Local Descriptor -- 2.2 Proposed WLGD -- 3 Experimental Results -- 3.1 Experiments on ORL Database -- 3.2 Experiments on Infrared Face Database -- 4 Conclusion -- References -- Multi-task Attribute Joint Feature Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experimental Protocol and Results Analysis -- 4.1 Experiment Results and Discussion -- 4.2 Experiment Results and Discussion -- 5 Conclusion -- References -- Person-specific Face Spoofing Detection for Replay Attack Based on Gaze Estimation -- 1 Introduction -- 2 Proposed Face Spoofing Detection Method -- 2.1 Gaze Estimation -- Gaze Feature Extraction. -- Adaptive Linear Regression with Incremental Learning. -- 2.2 Liveness Judgement -- 3 Experiments -- 3.1 Database -- 3.2 Experimental Results -- Effectiveness of Incremental Learning.
Effectiveness of Proposed Face Spoofing Detection Method.
Record Nr. UNISA-996466223703316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Biometric Recognition : 10th Chinese Conference, CCBR 2015, Tianjin, China, November 13-15, 2015, Proceedings / / edited by Jinfeng Yang, Jucheng Yang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng
Biometric Recognition : 10th Chinese Conference, CCBR 2015, Tianjin, China, November 13-15, 2015, Proceedings / / edited by Jinfeng Yang, Jucheng Yang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng
Edizione [1st ed. 2015.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Descrizione fisica 1 online resource (XVIII, 739 p. 377 illus. in color.)
Disciplina 006
Collana Image Processing, Computer Vision, Pattern Recognition, and Graphics
Soggetto topico Biometrics (Biology)
Pattern recognition
Optical data processing
Algorithms
Computer graphics
Application software
Biometrics
Pattern Recognition
Image Processing and Computer Vision
Algorithm Analysis and Problem Complexity
Computer Graphics
Information Systems Applications (incl. Internet)
ISBN 3-319-25417-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- Face -- Adaptive Quotient Image with 3D Generic Elastic Models for Pose and Illumination Invariant Face Recognition -- 1 Introduction -- 2 Adaptive Quotient Image -- 2.1 Quotient Image -- 2.2 Adaptive Quotient Image -- 3 3D Face Reconstruction by GEM -- 4 Face Recognition via Pose-Specific Metric -- 4.1 Pose Estimation and Alignment -- 4.2 Recognition via Pose-Specific Metric -- 5 Experiments and Results -- 5.1 Results -- 6 Conclusion -- References -- Low Rank Analysis of Eye Image Sequence - A Novel Basis for Face Liveness Detection -- 1 Introduction -- 2 Motivations -- 3 Proposed Method -- 3.1 Sample Noising Model -- 3.2 Solutions of the Noising Model -- 3.3 Basis for Classification -- 3.4 The Proposed Algorithm -- 4 Experiments -- 5 Conclusion -- References -- Non-negative Compatible Kernel Construction for Face Recognition -- 1 Introduction -- 2 Nonnegative Compatible Kernel Construction -- 2.1 Symmetric NMF -- 2.2 Nonnegative Interpolatory Basis Function Construction -- 2.3 Nonnegative Compatible Kernel Construction -- 3 Experimental Results -- 3.1 Experiments on ORL Database -- 3.2 Experiments on Pain Expression Database -- 4 Conclusions -- References -- 3D Face Recognition Using Local Features Matching on Sphere Depth Representation -- 1 Introduction -- 1.1 Related Work -- 1.2 Motivation and Approach Overview -- 2 Generation of Sphere Depth Image -- 3 Local Feature Extraction on Sphere Depth Image -- 3.1 Problem in Keypoints Selection -- 3.2 Ranking Keypoints in Keypoints Selection -- 4 Experiment Analysis -- 4.1 Experiment on Ran nking Model -- 4.2 Experiment on Pose Change 3D Faces Images -- 5 Conclusion -- References -- Face Recognition Using Local PCA Filters -- 1 Introduction -- 2 Method -- 2.1 Filter Learning -- 2.2 Feature Coding -- 3 Experiment -- 3.1 Experiment on Feret Database.
3.2 Experiment on LFW Database -- 4 Conclusion -- References -- Block Statistical Features-based Face Verification on Second Generation Identity Card -- 1 Introduction -- 2 Face Representation Based on LGBP -- 3 The Proposed Algorithm -- 3.1 Face Presentation Based on BSF -- 3.2 Energy Check on Gabor Filter -- 3.3 Face Verification Based on BSF -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Experiment with NEU-ID Database -- 5 Conclusion and Discussion -- References -- Towards Practical Face Recognition: A Local Binary Pattern Non Frontal Faces Filtering Approach -- 1 Introduction -- 2 Overall Design Framework -- 2.1 Face Detection -- 2.2 LBP Feature Extraction -- 3 Experiment -- 3.1 Establish Facial Pose Database -- 3.2 Experimental Procedure and Results -- 4 Summary and Prospect -- References -- Metric Learning Based False Positives Filtering for Face Detection -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Experiments on Our Wild Dataset -- 4.3 Experiments on FDDB -- 5 Conclusion -- References -- Face Recognition via Compact Fisher Vector -- 1 Introduction -- 2 Fisher Vector and Related Encoding Strategies -- 2.1 Fisher Kernel and Fisher Vector -- 2.2 Fisher Vector Normalization -- 2.3 Integrating Spatial Information -- 2.4 VLAD and Intra-Normalization -- 3 Compact Fisher Vector Representation -- 3.1 Sparsifying Fisher Vector -- 3.2 Fisher Vector with First Order Statistically Only -- 3.3 Residual Normalization -- 3.4 Tweaking Fisher Vector Representation -- 3.5 Normalization -- 4 Experiments -- 4.1 FERET -- 4.2 Labeled Faces in the Wild (LFW) -- 5 Conclusion -- References -- Nonlinear Metric Learning with Deep Convolutional Neural Network for Face Verification -- 1 Introduction -- 2 Related Work -- 2.1 Similarity Distance Metric Learning.
2.2 Deep Learning and Convolutional Neural Network -- 3 Proposed Method -- 3.1 Nonlinear Metric Learning with Deep ConvNet -- 3.2 Discrimination Similarity Distance Metric with Deep ConvNets -- 3.3 Implementation Details -- 4 Preliminary Experiment -- 4.1 Datasets and Experimental Settings -- 4.2 Comparison with Existing Deep Metric Learning Methods -- 4.3 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Locally Collaborative Representation in SimilarSubspace for Face Recognition -- 1 Introduction -- 2 Sparse Representation and Collaborative Representation -- 2.1 Sparse Representation Based Classification (SRC) -- 2.2 Collaborative Representation Based Classification (CRC) -- 3 Locally Collaborative Representation Based Classification -- 4 Experimental Results -- 5 Conclusion and Discussion -- References -- A DCNN and SDM Based Face Alignment Algorithm -- 1 Introduction -- 2 Coarsely Localize 5 Landmarks Based on DCNN -- 3 Finely Localize 68 Landmarks Based on SDM -- 3.1 Initialization -- 3.2 Finetune Landmarks -- 4 Experiments and Analysis -- 5 Conclusion -- References -- Robust Face Detection Based on Enhanced Local Sensitive Support Vector Machine -- 1 Introduction -- 2 Background: LSSVM -- 2.1 Discussion -- 3 Proposed Method -- 3.1 The Adaboost Based Background Filter -- 3.2 Locality-Sensitive SVM Using Kernel Combination -- 4 Experiments -- 4.1 Evaluation on CMU+MIT Dataset -- 4.2 Evaluation on FDDB Dataset -- 5 Conclusions -- References -- An Efficient Non-negative Matrix Factorization with Its Application to Face Recognition -- 1 Introduction -- 2 Traditional NMF -- 3 The Proposed NMF -- 4 Experimental Results -- 4.1 Comparisons on Convergence -- 4.2 Comparisons on Performance -- 5 Conclusions -- References -- Patch-based Sparse Dictionary Representation for Face Recognition with Single Sample per Person.
1 Introduction -- 2 Related Work -- 3 Our Proposed Method -- 4 Classification -- 5 Experiment -- 6 Conclusion -- References -- Non-negative Sparsity Preserving Projections Algorithm Based Face Recognition -- 1 Introduction -- 2 Algorithm Overview -- 2.1 Locality Preserving Projections -- 2.2 Non-negative Sparsity Preserving Projections -- 3 Experiments -- 3.1 Experiments on RL OR Face Database -- 3.2 Experiments on FERET Face Database -- 3.3 Experimental Analysis -- 4 Conclusions -- References -- WLD-TOP Based Algorithm against Face Spoofing Attacks -- 1 Introduction -- 2 Related Work -- 3 WLD from Three Orthogonal Planes (WLD-TOP) for Image Representation -- 3.1 Modified WLD -- 3.2 WLD-TOP -- 4 Experiments -- 4.1 Data Set -- 4.2 Results on the Intra-database -- 4.3 Results on the Cross-Database -- 4.4 Effectiveness of Each WLD-TOP Plane -- 5 Conclusion -- References -- Heterogeneous Face Recognition Based on Super Resolution Reconstruction by Adaptive Multi-dictionary Learning -- 1 Introduction -- 2 Sketch-to-Photo Transformation -- 3 Super-Resolution of Synthesized Photos -- 3.1 Super Resolution Reconstruction Based on Sparse Representation -- 3.2 Adaptive Multi-dictionary Learning -- 3.3 Training Samples Clustering -- 3.4 Multi-dictionary Learning -- 3.5 Super Resolution Reconstruction Model -- 4 Face Recognition Based on 2DMFA -- 4.1 Marginal Fisher Analysis -- 4.2 Two-Dimensional Marginal Fisher Analysis -- 5 Experiments -- 6 Conclusion -- References -- 3D Face Recognition Fusing Spherical Depth Map and Spherical Texture Map -- 1 Introduction -- 2 Pure Face Extraction -- 3 Recognition Process -- 3.1 Spherical Depth Map and Spherical Texture Map -- 3.2 Sparse Representation -- 4 Experiments -- 4.1 Database -- 4.2 Recognition -- 5 Conclusion -- References -- Privacy Preserving Face Identification in the Cloud through Sparse Representation.
1 Introduction -- 2 Background -- 2.1 Cryptography Primitives -- 2.2 SCiFI Overview -- 3 Privacy Preserving Face Identification -- 3.1 Modified Sparse Representation Based Face Identification -- 3.2 Private Face Identification Protocol -- 4 Experimental Results -- 5 Conclusion and Discussion -- References -- Infrared Face Recognition Based on ODP of Local Binary Patterns -- 1 Introduction -- 2 Discriminative Patterns Based on Local Binary Patterns -- 3 Optimized Discriminative Patterns (ODP) of LBP -- 4 The Multi-classifier Based on Voting Mechanism -- 5 Experiment Results -- 6 Conclusions -- References -- Image Classification Based on Discriminative Dictionary Pair Learning -- 1 Introduction -- 2 Discriminative Dictionary Pair Learning -- 3 Optimization -- 4 Classification Scheme -- 5 Experiments -- 5.1 Face Recognition -- 5.2 Handwritten Digit Recognition -- 6 Conclusion -- References -- Weber Local Gradient Pattern (WLGP) Method for Face Recognition -- Introduction -- 2 Proposed Method -- 2.1 Weber Local Descriptor -- 2.2 Proposed WLGD -- 3 Experimental Results -- 3.1 Experiments on ORL Database -- 3.2 Experiments on Infrared Face Database -- 4 Conclusion -- References -- Multi-task Attribute Joint Feature Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experimental Protocol and Results Analysis -- 4.1 Experiment Results and Discussion -- 4.2 Experiment Results and Discussion -- 5 Conclusion -- References -- Person-specific Face Spoofing Detection for Replay Attack Based on Gaze Estimation -- 1 Introduction -- 2 Proposed Face Spoofing Detection Method -- 2.1 Gaze Estimation -- Gaze Feature Extraction. -- Adaptive Linear Regression with Incremental Learning. -- 2.2 Liveness Judgement -- 3 Experiments -- 3.1 Database -- 3.2 Experimental Results -- Effectiveness of Incremental Learning.
Effectiveness of Proposed Face Spoofing Detection Method.
Record Nr. UNINA-9910483127703321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part IV / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part IV / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
Autore Liu Qingshan
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (514 pages)
Disciplina 006
Altri autori (Persone) WangHanzi
MaZhanyu
ZhengWeishi
ZhaHongbin
ChenXilin
WangLiang
JiRongrong
Collana Lecture Notes in Computer Science
Soggetto topico Image processing - Digital techniques
Computer vision
Artificial intelligence
Application software
Computer networks
Computer systems
Machine learning
Computer Imaging, Vision, Pattern Recognition and Graphics
Artificial Intelligence
Computer and Information Systems Applications
Computer Communication Networks
Computer System Implementation
Machine Learning
ISBN 981-9984-62-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Pattern Classification and Cluster Analysis -- Performance Evaluation and Benchmarks -- Remote Sensing Image Interpretation.
Record Nr. UNISA-996587868403316
Liu Qingshan  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part VI / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part VI / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
Autore Liu Qingshan
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (534 pages)
Disciplina 621.39
004.6
Altri autori (Persone) WangHanzi
MaZhanyu
ZhengWeishi
ZhaHongbin
ChenXilin
WangLiang
JiRongrong
Collana Lecture Notes in Computer Science
Soggetto topico Computer engineering
Computer networks
Image processing - Digital techniques
Computer vision
Computer systems
Machine learning
Computer Engineering and Networks
Computer Imaging, Vision, Pattern Recognition and Graphics
Computer Communication Networks
Computer System Implementation
Machine Learning
ISBN 981-9985-37-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Computational Photography, Sensing and Display Technology -- Video Analysis and Understanding -- Vision Applications and Systems.
Record Nr. UNISA-996587868603316
Liu Qingshan  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part IX / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part IX / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
Autore Liu Qingshan
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (520 pages)
Disciplina 006
Altri autori (Persone) WangHanzi
MaZhanyu
ZhengWeishi
ZhaHongbin
ChenXilin
WangLiang
JiRongrong
Collana Lecture Notes in Computer Science
Soggetto topico Image processing - Digital techniques
Computer vision
Artificial intelligence
Application software
Computer networks
Computer systems
Machine learning
Computer Imaging, Vision, Pattern Recognition and Graphics
Artificial Intelligence
Computer and Information Systems Applications
Computer Communication Networks
Computer System Implementation
Machine Learning
ISBN 981-9985-46-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part IX -- Neural Network and Deep Learning II -- Decoupled Contrastive Learning for Long-Tailed Distribution -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Online Tail Samples Discovery -- 3.2 Hard Negatives Generation -- 3.3 Contrastive Loss Reweighting -- 4 Experiments -- 4.1 Linear Probing Evaluations -- 4.2 Analysis -- 5 Conclusion -- References -- MFNet: A Channel Segmentation-Based Hierarchical Network for Multi-food Recognition -- 1 Introduction -- 2 Related Work -- 3 Food Image Datasets Construction -- 3.1 Food Images Collection -- 3.2 Annotation and Statistics -- 4 Method -- 4.1 CWF: Channel-Level Whole Image Food Information Acquisition -- 4.2 SGC: Spatial-Level Global Information Constraints -- 4.3 SPF: Spatial-Level Part Image Food Information Acquisition -- 5 Experiments -- 5.1 Datasets and Evaluation Metrics -- 5.2 Implementation Details -- 5.3 Performance Comparison with Other Method -- 5.4 Ablation Study -- 5.5 Visualization Result -- 6 Conclusion -- References -- Improving the Adversarial Robustness of Object Detection with Contrastive Learning -- 1 Introduction -- 2 Related Work -- 2.1 Adversarial Attacks -- 2.2 Adversarial Defenses -- 2.3 Contrastive Learning -- 3 Proposed Method -- 3.1 Contrastive Learning Module -- 3.2 Contrastive Adversarial SSD -- 3.3 Contrastive Adversarial YOLO -- 3.4 Adversarial Training with Contrastive Learning -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Defense Capability Evaluation -- 5 Conclusion -- References -- CAWNet: A Channel Attention Watermarking Attack Network Based on CWABlock -- 1 Introduction -- 2 Related Work -- 2.1 Attacked Watermarking Algorithm -- 2.2 Watermarking Attack Techniques -- 3 The Proposed Method -- 3.1 CAWNet -- 3.2 Attention Mechanism -- 3.3 CWABlock -- 4 Experiments and Results Analysis.
4.1 Evaluation Criteria -- 4.2 Ablation Experiment -- 4.3 Effects of Traditional Attack and Different Deep Learning Attack Methods -- 4.4 Stability and Suitability Testing -- 5 Conclusion -- References -- Global Consistency Enhancement Network for Weakly-Supervised Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Middle-Level Feature Auxiliary -- 3.2 Intra-class Consistency Enhancement -- 3.3 Critical Region Suppression -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Implementation Details -- 4.3 CAM Performance -- 4.4 Segmentation Performance -- 4.5 Ablation Study -- 5 Conclusion -- References -- Enhancing Model Robustness Against Adversarial Attacks with an Anti-adversarial Module -- 1 Introduction -- 2 Related Works -- 2.1 Gradient Masking -- 2.2 Adversarial Examples Detection -- 2.3 Robust Optimization -- 3 Methods -- 3.1 Counter-Adversarial Module -- 3.2 Enhancing Defense Against Black-Box Attacks -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Main Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- FGPTQ-ViT: Fine-Grained Post-training Quantization for Vision Transformers -- 1 Introduction -- 2 Related Work -- 2.1 CNN Quantization -- 2.2 Vision Transformer Quantization -- 3 Method -- 3.1 FGPTQ-ViT Framework -- 3.2 Fine-Grained ViT Quantization -- 3.3 Adaptive Piecewise Point Search Algorithm -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Experimental Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- Learning Hierarchical Representations in Temporal and Frequency Domains for Time Series Forecasting -- 1 Introduction -- 2 Related Work -- 2.1 Convolutional and Transformer Models -- 2.2 Fourier Transform and Decomposition Models -- 3 Proposed Approach -- 3.1 Time Series Hierarchical Decomposition -- 3.2 Trend Forecasting Module -- 3.3 Seasonal Forecasting Module -- 4 Experiments.
4.1 Dataset -- 4.2 Baselines and Setup -- 4.3 Implement Details and Evaluation Metrics -- 4.4 Main Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- DeCAB: Debiased Semi-supervised Learning for Imbalanced Open-Set Data -- 1 Introduction -- 2 Related Work -- 2.1 General SSL Methods -- 2.2 Imbalanced SSL Methods -- 2.3 Open-Set SSL Methods -- 3 Proposed Method -- 3.1 Problem Setting and Notations -- 3.2 Class-Aware Threshold -- 3.3 Selective Sample Reweighting -- 3.4 Positive-Pair Reweighting -- 3.5 Overall Training Objective -- 4 Experimental Results -- 4.1 Experimental Settings -- 4.2 Numerical Comparison -- 4.3 Analysis on Impact of OOD Data -- 4.4 Ablation Experiments -- 5 Conclusion -- A Analysis of the Effect of OOD Data to SSL Methods -- B Algorithm Flowchart -- C Visualized Comparison -- References -- An Effective Visible-Infrared Person Re-identification Network Based on Second-Order Attention and Mixed Intermediate Modality -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Second-Order Attention Module -- 3.2 Mixed Intermediate Modality Module -- 3.3 Optimization -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Art Methods -- 4.4 Ablation Study -- 5 Conclusion -- References -- Quadratic Polynomial Residual Network for No-Reference Image Quality Assessment -- 1 Introduction -- 2 Related Work -- 2.1 IQA and Information Entropy -- 2.2 IQA and Deep Learning -- 3 Design of Network -- 3.1 Two-Dimensional Information Entropy for Patch Sampling -- 3.2 Network Architecture -- 4 Experiment Result -- 5 Conclusion -- References -- Interactive Learning for Interpretable Visual Recognition via Semantic-Aware Self-Teaching Framework -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Patch Selection Strategy -- 3.2 Semantic-Aware Self-Teaching -- 4 Experiments.
5 Conclusion -- References -- Adaptive and Compact Graph Convolutional Network for Micro-expression Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Structure Graph -- 3 Method -- 3.1 Cheek Included Facial Graph -- 3.2 Tightly Connected Strategy -- 3.3 Small Region Module -- 3.4 Adaptive and Compact Graph Convolutional Network -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Quantitative Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- Consistency Guided Multiview Hypergraph Embedding Learning with Multiatlas-Based Functional Connectivity Networks Using Resting-State fMRI -- 1 Introduction -- 2 Methods -- 2.1 Hypergraph and Hypergraph Construction with FCN -- 2.2 Proposed CG-MHGEL with Multiatlas-Based FCNs -- 3 Experiment -- 3.1 Experimental Settings -- 3.2 Experimental Results and Analysis -- 4 Conclusion -- References -- A Diffusion Simulation User Behavior Perception Attention Network for Information Diffusion Prediction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Model Framework -- 3.3 Diffusion Simulation User Behavior Embedding -- 3.4 User Behavior Fusion Transformer -- 3.5 Cascade Perception Attention Network -- 3.6 Diffusion Prediction -- 4 Experiment -- 4.1 Results -- 5 Conclusion -- References -- A Representation Learning Link Prediction Approach Using Line Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 The NLG-GNN Framework -- 4 Experimental Setup -- 4.1 Experimental Results -- 5 Conclusion -- References -- Event Sparse Net: Sparse Dynamic Graph Multi-representation Learning with Temporal Attention for Event-Based Data -- 1 Introduction -- 2 Related Work -- 2.1 Graph Representations -- 2.2 Dynamic Graph Neural Network -- 3 Methods -- 3.1 Local Self Attention -- 3.2 Global Temporal Attention -- 4 Experiments -- 4.1 Datasets.
4.2 Setup -- 4.3 Continuous Data Inductive Learning -- 4.4 Discrete Data Inductive Learning -- 4.5 Discrete Data Transductive Learning -- 5 Conclusion -- References -- Federated Learning Based on Diffusion Model to Cope with Non-IID Data -- 1 Introduction -- 2 Method -- 2.1 The First Stage -- 2.2 The Second Stage -- 2.3 The Third Stage -- 3 Experiments -- 3.1 Setup -- 3.2 Performance Comparison -- 3.3 Experimental Factors Analysis -- 4 Conclusion -- References -- SFRSwin: A Shallow Significant Feature Retention Swin Transformer for Fine-Grained Image Classification of Wildlife Species -- 1 Introduction -- 2 Related Works -- 2.1 Convolutional Neural Network -- 2.2 Vision Transformer -- 3 Methodology -- 3.1 Self-attentive Mechanism Based on Shifted Windows -- 3.2 Random Data Enhancement -- 4 Evaluation -- 4.1 Datasets and Implementation Details -- 4.2 Model Complexity Analysis -- 5 Conclusion -- References -- A Robust and High Accurate Method for Hand Kinematics Decoding from Neural Populations -- 1 Introduction -- 2 Related Works -- 2.1 iBMI Cortical Control Decoding Algorithm -- 2.2 Attention Module -- 3 Method -- 3.1 Neural Recording System and Behavioral Task -- 3.2 Experimental Procedure of the Cortical Control -- 3.3 Temporal-Attention QRNN -- 3.4 Evaluation Metrics -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Comparison of Decoding Results -- 4.3 Discussion -- 5 Conclusion -- References -- Multi-head Attention Induced Dynamic Hypergraph Convolutional Networks -- 1 Introduction -- 2 Related Work -- 2.1 Neural Networks on Graph -- 2.2 Neural Networks on Hypergraph -- 3 Methodology -- 3.1 Definitions and Notations -- 3.2 Hypergraph Construction -- 3.3 Vertex Convolution -- 3.4 Hyperedge Convolution -- 3.5 The Proposed Algorithm -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Results and Discussion.
4.4 Ablation Studies.
Record Nr. UNISA-996587868503316
Liu Qingshan  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
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Lo trovi qui: Univ. di Salerno
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Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part V / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part V / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
Autore Liu Qingshan
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (542 pages)
Disciplina 621.39
004.6
Altri autori (Persone) WangHanzi
MaZhanyu
ZhengWeishi
ZhaHongbin
ChenXilin
WangLiang
JiRongrong
Collana Lecture Notes in Computer Science
Soggetto topico Computer engineering
Computer networks
Image processing - Digital techniques
Computer vision
Computer systems
Machine learning
Computer Engineering and Networks
Computer Imaging, Vision, Pattern Recognition and Graphics
Computer Communication Networks
Computer System Implementation
Machine Learning
ISBN 981-9984-69-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Biometric Recognition -- Face Recognition and Pose Recognition -- Structural Pattern Recognition.
Record Nr. UNISA-996587868903316
Liu Qingshan  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part VII / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part VII / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
Autore Liu Qingshan
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (525 pages)
Disciplina 006
Altri autori (Persone) WangHanzi
MaZhanyu
ZhengWeishi
ZhaHongbin
ChenXilin
WangLiang
JiRongrong
Collana Lecture Notes in Computer Science
Soggetto topico Image processing - Digital techniques
Computer vision
Artificial intelligence
Application software
Computer networks
Computer systems
Machine learning
Computer Imaging, Vision, Pattern Recognition and Graphics
Artificial Intelligence
Computer and Information Systems Applications
Computer Communication Networks
Computer System Implementation
Machine Learning
ISBN 981-9985-40-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Document Analysis and Recognition -- Feature Extraction and Feature Selection -- Multimedia Analysis and Reasoning.
Record Nr. UNISA-996587869003316
Liu Qingshan  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part X / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part X / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
Autore Liu Qingshan
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (509 pages)
Disciplina 006
Altri autori (Persone) WangHanzi
MaZhanyu
ZhengWeishi
ZhaHongbin
ChenXilin
WangLiang
JiRongrong
Collana Lecture Notes in Computer Science
Soggetto topico Image processing - Digital techniques
Computer vision
Artificial intelligence
Application software
Computer networks
Computer systems
Machine learning
Computer Imaging, Vision, Pattern Recognition and Graphics
Artificial Intelligence
Computer and Information Systems Applications
Computer Communication Networks
Computer System Implementation
Machine Learning
ISBN 981-9985-49-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part X -- Neural Network and Deep Learning III -- Dual-Stream Context-Aware Neural Network for Survival Prediction from Whole Slide Images -- 1 Introduction -- 2 Method -- 3 Experiments and Results -- 4 Conclusion -- References -- A Multi-label Image Recognition Algorithm Based on Spatial and Semantic Correlation Interaction -- 1 Introduction -- 2 Related Work -- 2.1 Correlation-Agnostic Algorithms -- 2.2 Spatial Correlation Algorithms -- 2.3 Semantic Correlation Algorithms -- 3 Methodology -- 3.1 Definition of Multi-label Image Recognition -- 3.2 The Framework of SSCI -- 3.3 Loss Function -- 4 Experiments -- 4.1 Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparison with Other Mainstream Algorithms -- 4.4 Evaluation of the SSCI Effectiveness -- 5 Conclusion -- References -- Hierarchical Spatial-Temporal Network for Skeleton-Based Temporal Action Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Temporal Action Segmentation -- 2.2 Skeleton-Based Action Recognition -- 3 Method -- 3.1 Network Architecture -- 3.2 Multi-Branch Transfer Fusion Module -- 3.3 Multi-Scale Temporal Convolution Module -- 3.4 Loss Function -- 4 Experiments -- 4.1 Setup -- 4.2 Effect of Hierarchical Model -- 4.3 Effect of Multiple Modalties -- 4.4 Effect of Multi-modal Fusion Methods -- 4.5 Effect of Multi-Scale Temporal Convolution -- 4.6 Comparision with State-of-the-Art -- 5 Conclusion -- References -- Multi-behavior Enhanced Graph Neural Networks for Social Recommendation -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methodology -- 4.1 Embedding Layer -- 4.2 Propagation Layer -- 4.3 Multi-behavior Integration Layer -- 4.4 Prediction Layer -- 4.5 Model Training -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Performance Comparison (RQ1) -- 5.3 Ablation Study (RQ2).
5.4 Parameter Analysis (RQ3) -- 6 Conclusion and Future Work -- References -- A Complex-Valued Neural Network Based Robust Image Compression -- 1 Introduction -- 2 Related Works -- 2.1 Neural Image Compression -- 2.2 Adversarial Attack -- 2.3 Complex-Valued Convolutional Neural Networks -- 3 Proposed Method -- 3.1 Overall Framework -- 3.2 Nonlinear Transform -- 4 Experiment Results -- 4.1 Experiment Setup -- 4.2 Results and Comparison -- 4.3 Ablation Study -- 5 Conclusions -- References -- Binarizing Super-Resolution Neural Network Without Batch Normalization -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Batch Normalization in SR Models -- 3.2 Channel-Wise Asymmetric Binarizer for Activations -- 3.3 Smoothness-Controlled Estimator -- 4 Experimentation -- 4.1 Experiment Setup -- 4.2 Ablation Study -- 4.3 Visualization -- 5 Conclusion -- References -- Infrared and Visible Image Fusion via Test-Time Training -- 1 Introduction -- 2 Method -- 2.1 Overall Framework -- 2.2 Training and Testing -- 3 Experiments -- 3.1 Experiment Configuration -- 3.2 Performance Comparison on TNO -- 3.3 Performance Comparison on VIFB -- 3.4 Ablation Study -- 4 Conclusion -- References -- Graph-Based Dependency-Aware Non-Intrusive Load Monitoring -- 1 Introduction -- 2 Proposed Method -- 2.1 Problem Formulation -- 2.2 Co-occurrence Probability Graph -- 2.3 Graph Structure Learning -- 2.4 Graph Attention Neural Network -- 2.5 Encoder-Decoder Module -- 3 Numerical Studies and Discussions -- 3.1 Dataset and Experiment Setup -- 3.2 Metrics and Comparisons -- 4 Conclusion -- References -- Few-Shot Object Detection via Classify-Free RPN -- 1 Introduction -- 2 Related Work -- 2.1 Object Detection -- 2.2 Few-Shot Learning -- 2.3 Few-Shot Object Detection -- 3 Methodology -- 3.1 Problem Setting -- 3.2 Analysis of the Base Class Bias Issue in RPN -- 3.3 Classify-Free RPN.
4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison with the State-of-the-Art -- 4.3 Ablation Study -- 5 Conclusion -- References -- IPFR: Identity-Preserving Face Reenactment with Enhanced Domain Adversarial Training and Multi-level Identity Priors -- 1 Introduction -- 2 Methods -- 2.1 Target Motion Encoder and 3D Shape Encoder -- 2.2 3D Shape-Aware Warping Module -- 2.3 Identity-Aware Refining Module -- 2.4 Enhanced Domain Discriminator -- 2.5 Training -- 3 Experiment -- 3.1 Experimental Setup -- 3.2 Comparisons -- 3.3 Ablation Study -- 4 Limitation -- 5 Conclusion -- References -- L2MNet: Enhancing Continual Semantic Segmentation with Mask Matching -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Preliminaries and Revisiting -- 3.2 Proposed Learn-to-Match Framework -- 3.3 Training Loss -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Quantitative Evaluation -- 4.3 Ablation Study -- 5 Conclusion -- References -- Adaptive Channel Pruning for Trainability Protection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Method Framework and Motivation -- 3.2 Channel Similarity Calculation and Trainability Preservation -- 3.3 Sparse Control and Optimization -- 4 Experiments -- 4.1 Experiments Settings and Evaluation Metrics -- 4.2 Results on Imagenet -- 4.3 Results on Cifar-10 -- 4.4 Results on YOLOX-s -- 4.5 Ablation -- 5 Conclusion -- References -- Exploiting Adaptive Crop and Deformable Convolution for Road Damage Detection -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Adaptive Image Cropping Based on Vanishing Point Estimation -- 3.2 Feature Learning with Deformable Convolution -- 3.3 Diagonal Intersection over Union Loss Function -- 4 Experiment -- 4.1 Comparative Analysis of Different Datasets -- 4.2 Ablation Analysis -- 5 Conclusion -- References -- Cascaded-Scoring Tracklet Matching for Multi-object Tracking.
1 Introduction -- 2 Related Work -- 2.1 Tracking by Detection -- 2.2 Joint Detection and Tracking -- 3 Proposed Method -- 3.1 Cascaded-Scoring Tracklet Matching -- 3.2 Motion-Guided Based Target Aware -- 3.3 Appearance-Assisted Feature Warper -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Ablation Studies -- 4.3 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Boosting Generalization Performance in Person Re-identification -- 1 Introduction -- 2 Related Work -- 2.1 Generalizable Person ReID -- 2.2 Vision-Language Learning -- 3 Method -- 3.1 Review of CLIP -- 3.2 A Novel Cross-Modal Framework -- 3.3 Prompt Design Process -- 3.4 Loss Function -- 4 Experiments -- 4.1 Datasets and Evaluation Protocols -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparison with State-of-the-Art Methods -- 4.5 Other Analysis -- 5 Conclusion -- References -- Self-guided Transformer for Video Super-Resolution -- 1 Introduction -- 2 Related Work -- 2.1 Video Super-Resolution -- 2.2 Vision Transformers -- 3 Our Method -- 3.1 Network Overview -- 3.2 Multi-headed Self-attention Module Based on Offset-Guided Window (OGW-MSA) -- 3.3 Feature Aggregation (FA) -- 4 Experiments -- 4.1 Datasets and Experimental Settings -- 4.2 Comparisons with State-of-the-Art Methods -- 4.3 Ablation Study -- 5 Conclusion -- References -- SAMP: Sub-task Aware Model Pruning with Layer-Wise Channel Balancing for Person Search -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Framework Overview -- 3.2 Sub-task Aware Channel Importance Estimation -- 3.3 Layer-Wise Channel Balancing -- 3.4 Adaptive OIM Loss for Model Pruning and Finetuning -- 4 Experimental Results and Analysis -- 4.1 Dataset and Evaluation Metric -- 4.2 Implementation Details -- 4.3 Comparison with the State-of-the-Art Approaches -- 4.4 Ablation Study -- 5 Conclusion.
References -- MKB: Multi-Kernel Bures Metric for Nighttime Aerial Tracking -- 1 Introduction -- 2 Methodology -- 2.1 Kernel Bures Metric -- 2.2 Multi-Kernel Bures Metric -- 2.3 Objective Loss -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Evaluation Datasets -- 3.3 Comparison Results -- 3.4 Visualization -- 3.5 Ablation Study -- 4 Conclusion -- References -- Deep Arbitrary-Scale Unfolding Network for Color-Guided Depth Map Super-Resolution -- 1 Introduction -- 2 The Proposed Method -- 2.1 Problem Formulation -- 2.2 Algorithm Unfolding -- 2.3 Continuous Up-Sampling Fusion (CUSF) -- 2.4 Loss Function -- 3 Experimental Results -- 3.1 Implementation Details -- 3.2 The Quality Comparison of Different DSR Methods -- 3.3 Ablation Study -- 4 Conclusion -- References -- SSDD-Net: A Lightweight and Efficient Deep Learning Model for Steel Surface Defect Detection -- 1 Introduction -- 2 Methods -- 2.1 LMFE: Light Multiscale Feature Extraction Module -- 2.2 SEFF: Simple Effective Feature Fusion Network -- 2.3 SSDD-Net -- 3 Experiments and Analysis -- 3.1 Implementation Details -- 3.2 Evaluation Metrics -- 3.3 Dataset -- 3.4 Ablation Studies -- 3.5 Comparison with Other SOTA Methods -- 3.6 Comprehensive Performance of SSDD-Net -- 4 Conclusion -- References -- Effective Small Ship Detection with Enhanced-YOLOv7 -- 1 Introduction -- 2 Method -- 2.1 Small Object-Aware Feature Extraction Module (SOAFE) -- 2.2 Small Object-Friendly Scale-Insensitive Regression Scheme (SOFSIR) -- 2.3 Geometric Constraint-Based Non-Maximum Suppression Method (GCNMS) -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Quantitative Analysis -- 3.3 Ablation Studies -- 3.4 Qualitative Analysis -- 4 Conclusion -- References -- PiDiNeXt: An Efficient Edge Detector Based on Parallel Pixel Difference Networks -- 1 Introduction -- 2 Related Work.
2.1 The Development of Deep Learning Based Edge Detection.
Record Nr. UNISA-996587868803316
Liu Qingshan  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
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Lo trovi qui: Univ. di Salerno
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