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Image Analysis and Processing - ICIAP 2022 : 21st International Conference, Lecce, Italy, May 23-27, 2022, Proceedings, Part I / / Stan Sclaroff [and four others], editors
Image Analysis and Processing - ICIAP 2022 : 21st International Conference, Lecce, Italy, May 23-27, 2022, Proceedings, Part I / / Stan Sclaroff [and four others], editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022]
Descrizione fisica 1 online resource (814 pages)
Disciplina 006.37
Collana Lecture notes in computer science
Soggetto topico Image analysis
Image processing
ISBN 3-031-06427-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Brave New Ideas -- A Lightweight Model for Satellite Pose Estimation -- 1 Introduction -- 2 Methodology and Data -- 2.1 Processing Pipeline -- 2.2 The Dataset -- 3 Experimental Results -- 4 Conclusions -- References -- Imitation Learning for Autonomous Vehicle Driving: How Does the Representation Matter? -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Benchmark -- 3.3 The Investigated CIL Method -- 3.4 Analysis of the Input Representation -- 3.5 Training Procedure -- 4 Experimental Results -- 5 Conclusions -- References -- LessonAble: Leveraging Deep Fakes in MOOC Content Creation -- 1 Introduction -- 2 Methodology -- 2.1 Voice Generation -- 2.2 Video Generation -- 2.3 Lip-syncing -- 3 Use Case -- 4 Conclusion -- References -- An Intelligent Scanning Vehicle for Waste Collection Monitoring -- 1 Introduction -- 2 Related Work -- 3 Intelligent Waste Recognition System -- 4 Hardware Design -- 5 Software Design -- 5.1 Data Preprocessing -- 5.2 Intelligent Waste Segmentation -- 5.3 Result Analysis and Communication -- 6 Results -- 7 Conclusion -- References -- Morphological Galaxies Classification According to Hubble-de Vaucouleurs Diagram Using CNNs -- 1 Introduction -- 2 Problem Statement -- 2.1 Galaxy Zoo Dataset -- 2.2 Data Augmentation -- 3 Method -- 3.1 Proposed Architecture -- 3.2 CNNs Based on Well-Known Backbones -- 4 Experimental Results -- 5 Conclusions -- References -- Biomedical and Assistive Technology -- Pulmonary-Restricted COVID-19 Informative Visual Screening Using Chest X-ray Images from Portable Devices -- 1 Introduction -- 2 Materials -- 3 Methodology -- 3.1 Region of Interest Extraction -- 3.2 COVID-19 Screening -- 4 Results and Discussion -- 4.1 Training Results -- 4.2 Test Results.
5 Conclusions -- References -- Comparison of Different Supervised and Self-supervised Learning Techniques in Skin Disease Classification -- 1 Introduction -- 2 Related Work -- 2.1 Self-supervised Learning -- 2.2 On ISIC 2019 Dataset -- 3 Method -- 3.1 Loss and Optimizer -- 3.2 Choosing CNN and Input Image Size -- 3.3 Data Augmentations -- 4 Experimental Results -- 4.1 Self-supervised Experiments -- 5 Conclusion and Future Work -- References -- Unsupervised Deformable Image Registration in a Landmark Scarcity Scenario: Choroid OCTA -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Network Architecture -- 2.3 Training Details -- 2.4 Baseline Methods -- 2.5 Evaluation Metric -- 3 Results and Discussion -- 3.1 Quantitative Evaluation -- 3.2 Qualitative Evaluation -- 4 Conclusions -- References -- Leveraging CycleGAN in Lung CT Sinogram-free Kernel Conversion -- 1 Introduction -- 2 Methodology -- 2.1 Pre-processing -- 2.2 Kernel Conversion -- 3 Experimental Setup -- 4 Results -- 5 Conclusion -- References -- Investigating One-Class Classifiers to Diagnose Alzheimer's Disease from Handwriting -- 1 Introduction -- 2 Related Works -- 3 One-class Classifiers -- 3.1 RNSA -- 3.2 Isolation Forest -- 3.3 One-class Support Vector Machine -- 4 Experimental Results -- 4.1 Dataset -- 4.2 Features Selection -- 4.3 Performance Evaluation -- 5 Discussion and Conclusions -- References -- Learning Unrolling-Based Neural Network for Magnetic Resonance Imaging Reconstruction -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning for MR Imaging -- 2.2 Unrolling-Based Deep Learning Approaches -- 3 Approach -- 3.1 Overall Architecture -- 3.2 Improved Swin Transformer Blocks of UTrans -- 4 Experiments -- 4.1 Datasets and Baseline Models -- 4.2 Implementation Details -- 4.3 Result and Evaluation -- 4.4 Ablation Analysis -- 5 Conclusion -- References.
Machine Learning to Predict Cognitive Decline of Patients with Alzheimer's Disease Using EEG Markers: A Preliminary Study -- 1 Introduction -- 2 EEG Features -- 3 Cognitive Tests and Scales -- 4 Experimental Results -- 5 Conclusions -- References -- Improving AMD Diagnosis by the Simultaneous Identification of Associated Retinal Lesions -- 1 Introduction -- 2 Materials and Methods -- 2.1 Prediction Loss -- 2.2 Network Architecture -- 2.3 Data -- 2.4 Training Details -- 3 Results and Discussion -- 4 Conclusions -- References -- Eye Diseases Classification Using Deep Learning -- 1 Introduction -- 1.1 Motivation -- 1.2 Overview of the Study -- 2 Related Work -- 2.1 Diabetic Retinopathy -- 2.2 Glaucoma -- 2.3 Cataract -- 3 Datasets -- 4 Synergic Deep Learning -- 5 Segmentation -- 6 Experiments Results -- 6.1 Initial Approach -- 6.2 Individual Diseases Classification -- 6.3 Multiple Diseases Classification -- 6.4 Comparison to Standard DCNN Model -- 7 Discussion -- 8 Conclusions -- References -- A Two-Step Radiologist-Like Approach for Covid-19 Computer-Aided Diagnosis from Chest X-Ray Images -- 1 Introduction -- 2 Datasets -- 3 Radiological Report -- 3.1 Architecture -- 3.2 Dealing with Uncertain Labels -- 4 COVID Diagnosis -- 5 Experiments -- 6 Conclusions -- References -- UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans -- 1 Introduction -- 2 Background and Related Works -- 3 The UniToChest Dataset -- 4 Methodology -- 4.1 Data Preprocessing -- 4.2 Network Architecture -- 4.3 Training Procedure -- 5 Results and Discussion -- 5.1 Nodules Segmentation -- 5.2 Detection -- 6 Conclusion and Future Works -- References -- Optimized Fusion of CNNs to Diagnose Pulmonary Diseases on Chest X-Rays -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Pre-processing -- 3.2 Training of Single CNNs -- 3.3 Ensemble Optimization.
4 Results and Discussions -- 5 Conclusions -- References -- High/Low Quality Style Transfer for Mutual Conversion of OCT Images Using Contrastive Unpaired Translation Generative Adversarial Networks -- 1 Introduction -- 2 Methodology -- 3 Results and Discussion -- 4 Conclusions -- References -- Real-Time Respiration Monitoring of Neonates from Thermography Images Using Deep Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Experimental Setup and Dataset -- 2.2 Data Preprocessing -- 2.3 Detector Training and Validation -- 2.4 Respiration Extraction -- 2.5 Real-Time Feasibility on Embedded GPUs -- 3 Results and Discussion -- 3.1 Detector Accuracy -- 3.2 Respiration Extraction -- 3.3 Inference Performance -- 4 Conclusion and Outlook -- References -- Improving Colon Carcinoma Grading by Advanced CNN Models -- 1 Introduction -- 2 Methods and Data -- 2.1 Advanced Deep Network Architectures -- 2.2 The Datasets: CRC and Extended CRC -- 3 Experimental Results on the Extended CRC Dataset -- 3.1 Comparisons to Leading Approaches in the Literature -- 4 Conclusion -- References -- Multimedia -- Frame Adaptive Rate Control Scheme for Video Compressive Sensing -- 1 Introduction -- 2 Proposed Frame Adaptive Rate Control Scheme -- 2.1 Overall System -- 2.2 Triangle Quantization for QP Initialization -- 2.3 Frame Adaptive QP Adjustment and Block-Based QP Refinement -- 3 Results and Comparison -- 3.1 Quantization Performance Comparison -- 3.2 Rate Control Performance Comparison -- 4 Conclusion -- References -- Shot-Based Hybrid Fusion for Movie Genre Classification -- 1 Introduction -- 2 Shot-Based Hybrid Fusion Networks -- 2.1 Video-Net -- 2.2 Audio-Net -- 2.3 Multi-modal Feature Fusion and Decision Fusion -- 3 Experiments and Results -- 3.1 Dataset and Data Preprocessing -- 3.2 Models Variations for Evaluation.
3.3 Experiment Settings and Evaluation Metrics -- 3.4 Results and Discussion -- 4 Conclusion -- References -- Landmark-Guided Conditional GANs for Face Aging -- 1 Introduction -- 2 Related Work -- 3 The Proposed Methods -- 3.1 Network Architecture -- 3.2 Objective Functions -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Qualitative Comparison -- 4.3 Quantitative Comparison -- 5 Conclusion -- References -- Introducing AV1 Codec-Level Video Steganography -- 1 Introduction -- 2 Related Works -- 3 Steganography on AV1 Codec-level -- 4 Experiments and Evaluation -- 4.1 Solution Capabilities -- 4.2 Visual Quality Preservation -- 4.3 Discussion -- 5 Conclusions and Future Works -- References -- Deep Learning -- Efficient Transfer Learning for Visual Tasks via Continuous Optimization of Prompts -- 1 Introduction -- 2 Methods -- 2.1 Pre-training -- 2.2 Fine-Tuning -- 2.3 Datasets -- 2.4 Training Procedure -- 3 Results -- 3.1 Classification -- 3.2 Few-Shot Classification -- 4 Discussion -- 4.1 Applications -- 4.2 Hyperparameters for VPT -- 4.3 Conclusion -- References -- Continual Learning with Neuron Activation Importance -- 1 Introduction -- 2 Proposed Method -- 2.1 Neuron Importance by Average Neuron Activation -- 2.2 Weight Re-initialization for Better Plasticity -- 3 Experimental Evaluations -- 3.1 MNIST -- 3.2 Split CIFAR10 -- 3.3 Split CIFAR10-100 -- 3.4 Split Tiny ImageNet -- 4 Conclusion -- References -- AD-CGAN: Contrastive Generative Adversarial Network for Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Proposed Approach -- 4.1 Contrastive GAN -- 4.2 Autoencoder -- 4.3 Latent Space Discriminator -- 4.4 Normality Score -- 5 Experimental Results -- 5.1 Datasets -- 5.2 Baseline Methods -- 5.3 Results -- 5.4 Ablation Study -- 6 Conclusion and Future Work -- References.
Analyzing EEG Data with Machine and Deep Learning: A Benchmark.
Record Nr. UNINA-9910568298603321
Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Image Analysis and Processing - ICIAP 2022 : 21st International Conference, Lecce, Italy, May 23-27, 2022, Proceedings, Part I / / Stan Sclaroff [and four others], editors
Image Analysis and Processing - ICIAP 2022 : 21st International Conference, Lecce, Italy, May 23-27, 2022, Proceedings, Part I / / Stan Sclaroff [and four others], editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022]
Descrizione fisica 1 online resource (814 pages)
Disciplina 006.37
Collana Lecture notes in computer science
Soggetto topico Image analysis
Image processing
ISBN 3-031-06427-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Brave New Ideas -- A Lightweight Model for Satellite Pose Estimation -- 1 Introduction -- 2 Methodology and Data -- 2.1 Processing Pipeline -- 2.2 The Dataset -- 3 Experimental Results -- 4 Conclusions -- References -- Imitation Learning for Autonomous Vehicle Driving: How Does the Representation Matter? -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Benchmark -- 3.3 The Investigated CIL Method -- 3.4 Analysis of the Input Representation -- 3.5 Training Procedure -- 4 Experimental Results -- 5 Conclusions -- References -- LessonAble: Leveraging Deep Fakes in MOOC Content Creation -- 1 Introduction -- 2 Methodology -- 2.1 Voice Generation -- 2.2 Video Generation -- 2.3 Lip-syncing -- 3 Use Case -- 4 Conclusion -- References -- An Intelligent Scanning Vehicle for Waste Collection Monitoring -- 1 Introduction -- 2 Related Work -- 3 Intelligent Waste Recognition System -- 4 Hardware Design -- 5 Software Design -- 5.1 Data Preprocessing -- 5.2 Intelligent Waste Segmentation -- 5.3 Result Analysis and Communication -- 6 Results -- 7 Conclusion -- References -- Morphological Galaxies Classification According to Hubble-de Vaucouleurs Diagram Using CNNs -- 1 Introduction -- 2 Problem Statement -- 2.1 Galaxy Zoo Dataset -- 2.2 Data Augmentation -- 3 Method -- 3.1 Proposed Architecture -- 3.2 CNNs Based on Well-Known Backbones -- 4 Experimental Results -- 5 Conclusions -- References -- Biomedical and Assistive Technology -- Pulmonary-Restricted COVID-19 Informative Visual Screening Using Chest X-ray Images from Portable Devices -- 1 Introduction -- 2 Materials -- 3 Methodology -- 3.1 Region of Interest Extraction -- 3.2 COVID-19 Screening -- 4 Results and Discussion -- 4.1 Training Results -- 4.2 Test Results.
5 Conclusions -- References -- Comparison of Different Supervised and Self-supervised Learning Techniques in Skin Disease Classification -- 1 Introduction -- 2 Related Work -- 2.1 Self-supervised Learning -- 2.2 On ISIC 2019 Dataset -- 3 Method -- 3.1 Loss and Optimizer -- 3.2 Choosing CNN and Input Image Size -- 3.3 Data Augmentations -- 4 Experimental Results -- 4.1 Self-supervised Experiments -- 5 Conclusion and Future Work -- References -- Unsupervised Deformable Image Registration in a Landmark Scarcity Scenario: Choroid OCTA -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Network Architecture -- 2.3 Training Details -- 2.4 Baseline Methods -- 2.5 Evaluation Metric -- 3 Results and Discussion -- 3.1 Quantitative Evaluation -- 3.2 Qualitative Evaluation -- 4 Conclusions -- References -- Leveraging CycleGAN in Lung CT Sinogram-free Kernel Conversion -- 1 Introduction -- 2 Methodology -- 2.1 Pre-processing -- 2.2 Kernel Conversion -- 3 Experimental Setup -- 4 Results -- 5 Conclusion -- References -- Investigating One-Class Classifiers to Diagnose Alzheimer's Disease from Handwriting -- 1 Introduction -- 2 Related Works -- 3 One-class Classifiers -- 3.1 RNSA -- 3.2 Isolation Forest -- 3.3 One-class Support Vector Machine -- 4 Experimental Results -- 4.1 Dataset -- 4.2 Features Selection -- 4.3 Performance Evaluation -- 5 Discussion and Conclusions -- References -- Learning Unrolling-Based Neural Network for Magnetic Resonance Imaging Reconstruction -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning for MR Imaging -- 2.2 Unrolling-Based Deep Learning Approaches -- 3 Approach -- 3.1 Overall Architecture -- 3.2 Improved Swin Transformer Blocks of UTrans -- 4 Experiments -- 4.1 Datasets and Baseline Models -- 4.2 Implementation Details -- 4.3 Result and Evaluation -- 4.4 Ablation Analysis -- 5 Conclusion -- References.
Machine Learning to Predict Cognitive Decline of Patients with Alzheimer's Disease Using EEG Markers: A Preliminary Study -- 1 Introduction -- 2 EEG Features -- 3 Cognitive Tests and Scales -- 4 Experimental Results -- 5 Conclusions -- References -- Improving AMD Diagnosis by the Simultaneous Identification of Associated Retinal Lesions -- 1 Introduction -- 2 Materials and Methods -- 2.1 Prediction Loss -- 2.2 Network Architecture -- 2.3 Data -- 2.4 Training Details -- 3 Results and Discussion -- 4 Conclusions -- References -- Eye Diseases Classification Using Deep Learning -- 1 Introduction -- 1.1 Motivation -- 1.2 Overview of the Study -- 2 Related Work -- 2.1 Diabetic Retinopathy -- 2.2 Glaucoma -- 2.3 Cataract -- 3 Datasets -- 4 Synergic Deep Learning -- 5 Segmentation -- 6 Experiments Results -- 6.1 Initial Approach -- 6.2 Individual Diseases Classification -- 6.3 Multiple Diseases Classification -- 6.4 Comparison to Standard DCNN Model -- 7 Discussion -- 8 Conclusions -- References -- A Two-Step Radiologist-Like Approach for Covid-19 Computer-Aided Diagnosis from Chest X-Ray Images -- 1 Introduction -- 2 Datasets -- 3 Radiological Report -- 3.1 Architecture -- 3.2 Dealing with Uncertain Labels -- 4 COVID Diagnosis -- 5 Experiments -- 6 Conclusions -- References -- UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans -- 1 Introduction -- 2 Background and Related Works -- 3 The UniToChest Dataset -- 4 Methodology -- 4.1 Data Preprocessing -- 4.2 Network Architecture -- 4.3 Training Procedure -- 5 Results and Discussion -- 5.1 Nodules Segmentation -- 5.2 Detection -- 6 Conclusion and Future Works -- References -- Optimized Fusion of CNNs to Diagnose Pulmonary Diseases on Chest X-Rays -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Pre-processing -- 3.2 Training of Single CNNs -- 3.3 Ensemble Optimization.
4 Results and Discussions -- 5 Conclusions -- References -- High/Low Quality Style Transfer for Mutual Conversion of OCT Images Using Contrastive Unpaired Translation Generative Adversarial Networks -- 1 Introduction -- 2 Methodology -- 3 Results and Discussion -- 4 Conclusions -- References -- Real-Time Respiration Monitoring of Neonates from Thermography Images Using Deep Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Experimental Setup and Dataset -- 2.2 Data Preprocessing -- 2.3 Detector Training and Validation -- 2.4 Respiration Extraction -- 2.5 Real-Time Feasibility on Embedded GPUs -- 3 Results and Discussion -- 3.1 Detector Accuracy -- 3.2 Respiration Extraction -- 3.3 Inference Performance -- 4 Conclusion and Outlook -- References -- Improving Colon Carcinoma Grading by Advanced CNN Models -- 1 Introduction -- 2 Methods and Data -- 2.1 Advanced Deep Network Architectures -- 2.2 The Datasets: CRC and Extended CRC -- 3 Experimental Results on the Extended CRC Dataset -- 3.1 Comparisons to Leading Approaches in the Literature -- 4 Conclusion -- References -- Multimedia -- Frame Adaptive Rate Control Scheme for Video Compressive Sensing -- 1 Introduction -- 2 Proposed Frame Adaptive Rate Control Scheme -- 2.1 Overall System -- 2.2 Triangle Quantization for QP Initialization -- 2.3 Frame Adaptive QP Adjustment and Block-Based QP Refinement -- 3 Results and Comparison -- 3.1 Quantization Performance Comparison -- 3.2 Rate Control Performance Comparison -- 4 Conclusion -- References -- Shot-Based Hybrid Fusion for Movie Genre Classification -- 1 Introduction -- 2 Shot-Based Hybrid Fusion Networks -- 2.1 Video-Net -- 2.2 Audio-Net -- 2.3 Multi-modal Feature Fusion and Decision Fusion -- 3 Experiments and Results -- 3.1 Dataset and Data Preprocessing -- 3.2 Models Variations for Evaluation.
3.3 Experiment Settings and Evaluation Metrics -- 3.4 Results and Discussion -- 4 Conclusion -- References -- Landmark-Guided Conditional GANs for Face Aging -- 1 Introduction -- 2 Related Work -- 3 The Proposed Methods -- 3.1 Network Architecture -- 3.2 Objective Functions -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Qualitative Comparison -- 4.3 Quantitative Comparison -- 5 Conclusion -- References -- Introducing AV1 Codec-Level Video Steganography -- 1 Introduction -- 2 Related Works -- 3 Steganography on AV1 Codec-level -- 4 Experiments and Evaluation -- 4.1 Solution Capabilities -- 4.2 Visual Quality Preservation -- 4.3 Discussion -- 5 Conclusions and Future Works -- References -- Deep Learning -- Efficient Transfer Learning for Visual Tasks via Continuous Optimization of Prompts -- 1 Introduction -- 2 Methods -- 2.1 Pre-training -- 2.2 Fine-Tuning -- 2.3 Datasets -- 2.4 Training Procedure -- 3 Results -- 3.1 Classification -- 3.2 Few-Shot Classification -- 4 Discussion -- 4.1 Applications -- 4.2 Hyperparameters for VPT -- 4.3 Conclusion -- References -- Continual Learning with Neuron Activation Importance -- 1 Introduction -- 2 Proposed Method -- 2.1 Neuron Importance by Average Neuron Activation -- 2.2 Weight Re-initialization for Better Plasticity -- 3 Experimental Evaluations -- 3.1 MNIST -- 3.2 Split CIFAR10 -- 3.3 Split CIFAR10-100 -- 3.4 Split Tiny ImageNet -- 4 Conclusion -- References -- AD-CGAN: Contrastive Generative Adversarial Network for Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Proposed Approach -- 4.1 Contrastive GAN -- 4.2 Autoencoder -- 4.3 Latent Space Discriminator -- 4.4 Normality Score -- 5 Experimental Results -- 5.1 Datasets -- 5.2 Baseline Methods -- 5.3 Results -- 5.4 Ablation Study -- 6 Conclusion and Future Work -- References.
Analyzing EEG Data with Machine and Deep Learning: A Benchmark.
Record Nr. UNISA-996475771903316
Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Image analysis and processing -- ICIAP 2022 : 21st international conference, Lecce, Italy, May 23-27, 2022, proceedings : part III / / edited by Stan Sclaroff [and four others]
Image analysis and processing -- ICIAP 2022 : 21st international conference, Lecce, Italy, May 23-27, 2022, proceedings : part III / / edited by Stan Sclaroff [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (507 pages)
Disciplina 006.6
Collana Lecture Notes in Computer Science
Soggetto topico Image analysis
Image processing - Digital techniques
ISBN 3-031-06433-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996475771503316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Image analysis and processing -- ICIAP 2022 . Part II : 21st international conference, Lecce, Italy, May 23-27, 2022 : proceedings / / Stan Sclaroff [and four others]
Image analysis and processing -- ICIAP 2022 . Part II : 21st international conference, Lecce, Italy, May 23-27, 2022 : proceedings / / Stan Sclaroff [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer International Publishing, , [2022]
Descrizione fisica 1 online resource (786 pages)
Disciplina 621.367
Collana Lecture Notes in Computer Science
Soggetto topico Image analysis
Image processing - Digital techniques
ISBN 3-031-06430-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996475770803316
Cham, Switzerland : , : Springer International Publishing, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Image analysis and processing -- ICIAP 2022 . Part II : 21st international conference, Lecce, Italy, May 23-27, 2022 : proceedings / / Stan Sclaroff [and four others]
Image analysis and processing -- ICIAP 2022 . Part II : 21st international conference, Lecce, Italy, May 23-27, 2022 : proceedings / / Stan Sclaroff [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer International Publishing, , [2022]
Descrizione fisica 1 online resource (786 pages)
Disciplina 621.367
Collana Lecture Notes in Computer Science
Soggetto topico Image analysis
Image processing - Digital techniques
ISBN 3-031-06430-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910569193503321
Cham, Switzerland : , : Springer International Publishing, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Image analysis and processing -- ICIAP 2022 : 21st international conference, Lecce, Italy, May 23-27, 2022, proceedings : part III / / edited by Stan Sclaroff [and four others]
Image analysis and processing -- ICIAP 2022 : 21st international conference, Lecce, Italy, May 23-27, 2022, proceedings : part III / / edited by Stan Sclaroff [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (507 pages)
Disciplina 006.6
Collana Lecture Notes in Computer Science
Soggetto topico Image analysis
Image processing - Digital techniques
ISBN 3-031-06433-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910568290503321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Pattern Recognition. ICPR International Workshops and Challenges [[electronic resource] ] : Virtual Event, January 10–15, 2021, Proceedings, Part VI / / edited by Alberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani
Pattern Recognition. ICPR International Workshops and Challenges [[electronic resource] ] : Virtual Event, January 10–15, 2021, Proceedings, Part VI / / edited by Alberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (XX, 821 p. 323 illus., 277 illus. in color.)
Disciplina 006.37
Collana Image Processing, Computer Vision, Pattern Recognition, and Graphics
Soggetto topico Computer vision
Application software
Artificial intelligence
Computers
Computer Vision
Computer and Information Systems Applications
Artificial Intelligence
Computing Milieux
ISBN 3-030-68780-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996464428603316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Pattern Recognition. ICPR International Workshops and Challenges : Virtual Event, January 10–15, 2021, Proceedings, Part VI / / edited by Alberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani
Pattern Recognition. ICPR International Workshops and Challenges : Virtual Event, January 10–15, 2021, Proceedings, Part VI / / edited by Alberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (XX, 821 p. 323 illus., 277 illus. in color.)
Disciplina 006.37
006.4
Collana Image Processing, Computer Vision, Pattern Recognition, and Graphics
Soggetto topico Computer vision
Application software
Artificial intelligence
Computers
Computer Vision
Computer and Information Systems Applications
Artificial Intelligence
Computing Milieux
ISBN 3-030-68780-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910483556603321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Visual Saliency: From Pixel-Level to Object-Level Analysis / / by Jianming Zhang, Filip Malmberg, Stan Sclaroff
Visual Saliency: From Pixel-Level to Object-Level Analysis / / by Jianming Zhang, Filip Malmberg, Stan Sclaroff
Autore Zhang Jianming (Research scientist)
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (138 pages)
Disciplina 621.367
006.42
Soggetto topico Optical data processing
Signal processing
Image processing
Speech processing systems
Computer science - Mathematics
Image Processing and Computer Vision
Signal, Image and Speech Processing
Mathematics of Computing
ISBN 3-030-04831-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1 Overview -- 2 Boolean Map Saliency: A Surprisingly Simple Method -- 3 A Distance Transform Perspective -- 4 Efficient Distance Transform for Salient Region Detection -- 5 Salient Object Subitizing -- 6 Unconstrained Salient Object Detection -- 7 Conclusion and Future Work.
Record Nr. UNINA-9910337571303321
Zhang Jianming (Research scientist)  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui