Kooperativer Bibliotheksverbund

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Umfang: 1 online resource (584 pages)

ISBN: 0-12-822149-6

Serie: Computer Vision and Pattern Recognition

Inhalt: "Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5-10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection."--

Anmerkung: Front Cover -- Advanced Methods and Deep Learning in Computer Vision -- Copyright -- Contents -- List of contributors -- About the editors -- Preface -- 1 The dramatically changing face of computer vision -- 1.1 Introduction - computer vision and its origins -- 1.2 Part A - Understanding low-level image processing operators -- 1.2.1 The basics of edge detection -- 1.2.2 The Canny operator -- 1.2.3 Line segment detection -- 1.2.4 Optimizing detection sensitivity -- 1.2.5 Dealing with variations in the background intensity -- 1.2.6 A theory combining the matched filter and zero-mean constructs -- 1.2.7 Mask design-other considerations -- 1.2.8 Corner detection -- 1.2.9 The Harris `interest point' operator -- 1.3 Part B - 2-D object location and recognition -- 1.3.1 The centroidal profile approach to shape analysis -- 1.3.2 Hough-based schemes for object detection -- 1.3.3 Application of the Hough transform to line detection -- 1.3.4 Using RANSAC for line detection -- 1.3.5 A graph-theoretic approach to object location -- 1.3.6 Using the generalized Hough transform (GHT) to save computation -- 1.3.7 Part-based approaches -- 1.4 Part C - 3-D object location and the importance of invariance -- 1.4.1 Introduction to 3-D vision -- 1.4.2 Pose ambiguities under perspective projection -- 1.4.3 Invariants as an aid to 3-D recognition -- 1.4.4 Cross ratios: the `ratio of ratios' concept -- 1.4.5 Invariants for noncollinear points -- 1.4.6 Vanishing point detection -- 1.4.7 More on vanishing points -- 1.4.8 Summary: the value of invariants -- 1.4.9 Image transformations for camera calibration -- 1.4.10 Camera calibration -- 1.4.11 Intrinsic and extrinsic parameters -- 1.4.12 Multiple view vision -- 1.4.13 Generalized epipolar geometry -- 1.4.14 The essential matrix -- 1.4.15 The fundamental matrix -- 1.4.16 Properties of the essential and fundamental matrices. , 1.4.17 Estimating the fundamental matrix -- 1.4.18 Improved methods of triangulation -- 1.4.19 The achievements and limitations of multiple view vision -- 1.5 Part D - Tracking moving objects -- 1.5.1 Tracking - the basic concept -- 1.5.2 Alternatives to background subtraction -- 1.6 Part E - Texture analysis -- 1.6.1 Introduction -- 1.6.2 Basic approaches to texture analysis -- 1.6.3 Laws' texture energy approach -- 1.6.4 Ade's eigenfilter approach -- 1.6.5 Appraisal of the Laws and Ade approaches -- 1.6.6 More recent developments -- 1.7 Part F - From artificial neural networks to deep learning methods -- 1.7.1 Introduction: how ANNs metamorphosed into CNNs -- 1.7.2 Parameters for defining CNN architectures -- 1.7.3 Krizhevsky et al.'s AlexNet architecture -- 1.7.4 Simonyan and Zisserman's VGGNet architecture -- 1.7.5 Noh et al.'s DeconvNet architecture -- 1.7.6 Badrinarayanan et al.'s SegNet architecture -- 1.7.7 Application of deep learning to object tracking -- 1.7.8 Application of deep learning to texture classification -- 1.7.9 Texture analysis in the world of deep learning -- 1.8 Part G - Summary -- Acknowledgments -- References -- Biographies -- 2 Advanced methods for robust object detection -- 2.1 Introduction -- 2.2 Preliminaries -- 2.3 R-CNN -- 2.3.1 System design -- 2.3.2 Training -- 2.4 SPP-Net -- 2.5 Fast R-CNN -- 2.5.1 Architecture -- 2.5.2 RoI pooling -- 2.5.3 Multitask loss -- 2.5.4 Finetuning strategy -- 2.6 Faster R-CNN -- 2.6.1 Architecture -- 2.6.2 Region proposal networks -- 2.7 Cascade R-CNN -- 2.7.1 Architecture -- 2.7.2 Cascaded bounding box regression -- 2.7.3 Cascaded detection -- 2.8 Multiscale feature representation -- 2.8.1 MS-CNN -- 2.8.1.1 Architecture -- 2.8.2 FPN -- 2.8.2.1 Architecture -- Bottom-up pathway -- Top-down pathway and lateral connections -- 2.9 YOLO -- 2.10 SSD -- 2.10.1 Architecture -- 2.10.2 Training. , 2.11 RetinaNet -- 2.11.1 Focal loss -- 2.12 Detection performances -- 2.13 Conclusion -- References -- Biographies -- 3 Learning with limited supervision -- 3.1 Introduction -- 3.2 Context-aware active learning -- 3.2.1 Active learning -- 3.2.2 Context in active learning -- 3.2.3 Framework for context-aware active learning -- 3.2.4 Applications -- 3.3 Weakly supervised event localization -- 3.3.1 Network architecture -- 3.3.2 k-max multiple instance learning -- 3.3.3 Coactivity similarity -- 3.3.4 Applications -- 3.4 Domain adaptation of semantic segmentation using weak labels -- 3.4.1 Weak labels for category classification -- 3.4.2 Weak labels for feature alignment -- 3.4.3 Network optimization -- 3.4.4 Acquiring weak labels -- 3.4.5 Applications -- 3.4.6 Output space visualization -- 3.5 Weakly-supervised reinforcement learning for dynamical tasks -- 3.5.1 Learning subgoal prediction -- 3.5.2 Supervised pretraining -- 3.5.3 Applications -- 3.6 Conclusions -- Acknowledgments -- References -- Biographies -- 4 Efficient methods for deep learning -- 4.1 Model compression -- 4.1.1 Parameter pruning -- 4.1.2 Low-rank factorization -- 4.1.3 Quantization -- 4.1.4 Knowledge distillation -- 4.1.5 Automated model compression -- 4.2 Efficient neural network architectures -- 4.2.1 Standard convolution layer -- 4.2.2 Efficient convolution layers -- 4.2.3 Manually designed efficient CNN models -- 4.2.4 Neural architecture search -- 4.2.5 Hardware-aware neural architecture search -- 4.3 Conclusion -- References -- 5 Deep conditional image generation -- 5.1 Introduction -- 5.2 Visual pattern learning: a brief review -- 5.3 Classical generative models -- 5.4 Deep generative models -- 5.5 Deep conditional image generation -- 5.6 Disentanglement for controllable synthesis -- 5.6.1 Disentangle visual content and style -- 5.6.2 Disentangle structure and style. , 5.6.3 Disentangle identity and attributes -- 5.7 Conclusion and discussions -- References -- 6 Deep face recognition using full and partial face images -- 6.1 Introduction -- 6.1.1 Deep learning models -- 6.1.1.1 The structure of a CNN -- 6.1.1.2 Methods of training CNNs -- 6.1.1.3 Datasets for deep face recognition experimentation -- 6.2 Components of deep face recognition -- 6.2.1 An example of a trained CNN model for face recognition -- 6.2.1.1 Feature extraction -- 6.2.1.2 Feature classification -- 6.3 Face recognition using full face images -- 6.3.1 Similarity matching using the FaceNet model -- 6.4 Deep face recognition using partial face data -- 6.5 Specific model training for full and partial faces -- 6.5.1 Suggested architecture of the model -- 6.5.2 Training phase -- 6.6 Discussion and conclusions -- References -- Biographies -- 7 Unsupervised domain adaptation using shallow and deep representations -- 7.1 Introduction -- 7.2 Unsupervised domain adaptation using manifolds -- 7.2.1 Unsupervised domain adaptation using product manifolds -- 7.3 Unsupervised domain adaptation using dictionaries -- 7.3.1 Generalized domain adaptive dictionary learning -- 7.3.2 Joint hierarchical domain adaptation and feature learning -- 7.3.3 Incremental dictionary learning for unsupervised domain adaptation -- 7.4 Unsupervised domain adaptation using deep networks -- 7.4.1 Discriminative approaches for domain adaptation -- 7.4.2 Generative approaches for domain adaptation -- 7.5 Summary -- References -- Biographies -- 8 Domain adaptation and continual learning in semantic segmentation -- 8.1 Introduction -- 8.1.1 Problem formulation -- 8.2 Unsupervised domain adaptation -- 8.2.1 Domain adaptation problem formulation -- 8.2.2 Adaptation focus -- 8.2.2.1 Input level adaptation -- 8.2.2.2 Feature level adaptation -- 8.2.2.3 Output level adaptation. , 8.2.3 Unsupervised domain adaptation techniques -- 8.2.3.1 Domain adversarial adaptation -- 8.2.3.2 Generative-based adaptation -- 8.2.3.3 Classifier discrepancy -- 8.2.3.4 Self-supervised learning -- Self-training -- Entropy minimization -- 8.2.3.5 Multitasking -- 8.3 Continual learning -- 8.3.1 Continual learning problem formulation -- 8.3.2 Continual learning setups in semantic segmentation -- 8.3.3 Incremental learning techniques -- 8.3.3.1 Knowledge distillation -- 8.3.3.2 Parameter freezing -- 8.3.3.3 Geometrical feature-level regularization -- 8.3.3.4 New directions -- 8.4 Conclusion -- Acknowledgment -- References -- Biographies -- 9 Visual tracking -- 9.1 Introduction -- 9.1.1 Problem definition -- 9.1.2 Challenges in tracking -- 9.1.3 Motivation of the setting -- 9.1.4 Historical development -- 9.2 Template-based methods -- 9.2.1 The basics -- 9.2.2 Performance measures -- 9.2.3 Normalized cross correlation -- 9.2.4 Phase-only matched filter -- 9.3 Online-learning-based methods -- 9.3.1 The MOSSE filter -- 9.3.2 Discriminative correlation filters -- 9.3.3 Suitable features for DCFs -- 9.3.4 Scale space tracking -- 9.3.5 Spatial and temporal weighting -- 9.4 Deep learning-based methods -- 9.4.1 Deep features in DCFs -- 9.4.2 Adaptive deep features -- 9.4.3 End-to-end learning DCFs -- 9.5 The transition from tracking to segmentation -- 9.5.1 Video object segmentation -- 9.5.2 A generative VOS method -- 9.5.3 A discriminative VOS method -- 9.6 Conclusions -- Acknowledgment -- References -- Biographies -- 10 Long-term deep object tracking -- 10.1 Introduction -- 10.1.1 Challenges in video object tracking -- 10.1.1.1 Visual challenges in tracking -- 10.1.1.2 Learning challenges in tracking -- 10.1.1.3 Engineering challenges in tracking -- 10.2 Short-term visual object tracking -- 10.2.1 Shallow trackers -- 10.2.2 Deep trackers. , 10.2.2.1 Correlation filter-based tracking.

Weitere Ausg.: ISBN 0-12-822109-7

Sprache: Englisch