{"product_id":"advanced-methods-and-deep-learning-in-computer-vision-9780128221099","title":"Advanced Methods and Deep Learning in Computer Vision","description":"\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e   List of contributors xi   About the editors xiii   Preface xv   1. The dramatically changing face of computer vision   E.R. DAVIES   1.1 Introduction – computer vision and its origins 1   1.2 Part A – Understanding low-level image processing operators 4   1.3 Part B – 2-D object location and recognition 15   1.4 Part C – 3-D object location and the importance of invariance 29   1.5 Part D – Tracking moving objects 55   1.6 Part E – Texture analysis 61   1.7 Part F – From artificial neural networks to deep learning methods 68   1.8 Part G – Summary 86   References 87   2. Advanced methods for robust object detection   ZHAOWEI CAI AND NUNO VASCONCELOS   2.1 Introduction 93   2.2 Preliminaries 95   2.3 R-CNN 96   2.4 SPP-Net 97   2.5 Fast R-CNN 98   2.6 Faster R-CNN 101   2.7 Cascade R-CNN 103   2.8 Multiscale feature representation 106   2.9 YOLO 110   2.10 SSD 112   2.11 RetinaNet 113   2.12 Detection performances 115   2.13 Conclusion 115   References 116   3. Learning with limited supervision   SUJOY PAUL AND AMIT K. ROY-CHOWDHURY   3.1 Introduction 119   3.2 Context-aware active learning 120   3.3 Weakly supervised event localization 129   3.4 Domain adaptation of semantic segmentation using weak labels 137   3.5 Weakly-supervised reinforcement learning for dynamical tasks 144   3.6 Conclusions 151   References 153   4. Efficient methods for deep learning   HAN CAI, JI LIN, AND SONG HAN   4.1 Model compression 159   4.2 Efficient neural network architectures 170   4.3 Conclusion 185   References 185   5. Deep conditional image generation   GANG HUA AND DONGDONG CHEN   5.1 Introduction 191   5.2 Visual pattern learning: a brief review 194   5.3 Classical generative models 195   5.4 Deep generative models 197   5.5 Deep conditional image generation 200   5.6 Disentanglement for controllable synthesis 201   5.7 Conclusion and discussions 216   References 216   6. Deep face recognition using full and partial face images   HASSAN UGAIL   6.1 Introduction 221   6.2 Components of deep face recognition 227   6.3 Face recognition using full face images 231   6.4 Deep face recognition using partial face data 233   6.5 Specific model training for full and partial faces 237   6.6 Discussion and conclusions 239   References 240   7. Unsupervised domain adaptation using shallow and deep representations   YOGESH BALAJI, HIEN NGUYEN, AND RAMA CHELLAPPA   7.1 Introduction 243   7.2 Unsupervised domain adaptation using manifolds 244   7.3 Unsupervised domain adaptation using dictionaries 247   7.4 Unsupervised domain adaptation using deep networks 258   7.5 Summary 270   References 270   8. Domain adaptation and continual learning in semantic segmentation   UMBERTO MICHIELI, MARCO TOLDO, AND PIETRO ZANUTTIGH   8.1 Introduction 275   8.2 Unsupervised domain adaptation 277   8.3 Continual learning 291   8.4 Conclusion 298   References 299   9. Visual tracking   MICHAEL FELSBERG   9.1 Introduction 305   9.2 Template-based methods 308   9.3 Online-learning-based methods 314   9.4 Deep learning-based methods 323   9.5 The transition from tracking to segmentation 327   9.6 Conclusions 331   References 332   10. Long-term deep object tracking   EFSTRATIOS GAVVES AND DEEPAK GUPTA   10.1 Introduction 337   10.2 Short-term visual object tracking 341   10.3 Long-term visual object tracking 345   10.4 Discussion 367   References 368   11. Learning for action-based scene understanding   CORNELIA FERMÜLLER AND MICHAEL MAYNORD   11.1 Introduction 373   11.2 Affordances of objects 375   11.3 Functional parsing of manipulation actions 383   11.4 Functional scene understanding through deep learning with language and vision 390   11.5 Future directions 397   11.6 Conclusions 399   References 399   12. Self-supervised temporal event segmentation inspired by cognitive theories   RAMY MOUNIR, SATHYANARAYANAN AAKUR, AND SUDEEP SARKAR   12.1 Introduction 406   12.2 The event segmentation theory from cognitive science 408   12.3 Version 1: single-pass temporal segmentation using prediction 410   12.4 Version 2: segmentation using attention-based event models 421   12.5 Version 3: spatio-temporal localization using prediction loss map 428   12.6 Other event segmentation approaches in computer vision 440   12.7 Conclusions 443   References 444   13. Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware   systems   CARLO REGAZZONI, ALI KRAYANI, GIULIA SLAVIC, AND LUCIO MARCENARO   13.1 Introduction 450   13.2 Base concepts and state of the art 451   13.3 Framework for computing anomaly in self-aware systems 458   13.4 Case study results: anomaly detection on multisensory data from a self-aware vehicle 467   13.5 Conclusions 476   References 477   14. Deep plug-and-play and deep unfolding methods for image restoration   KAI ZHANG AND RADU TIMOFTE   14.1 Introduction 481   14.2 Half quadratic splitting (HQS) algorithm 484   14.3 Deep plug-and-play image restoration 485   14.4 Deep unfolding image restoration 492   14.5 Experiments 495   14.6 Discussion and conclusions 504   References 505   15. Visual adversarial attacks and defenses   CHANGJAE OH, ALESSIO XOMPERO, AND ANDREA CAVALLARO   15.1 Introduction 511   15.2 Problem definition 512   15.3 Properties of an adversarial attack 514   15.4 Types of perturbations 515   15.5 Attack scenarios 515   15.6 Image processing 522   15.7 Image classification 523   15.8 Semantic segmentation and object detection 529   15.9 Object tracking 529   15.10 Video classification 531   15.11 Defenses against adversarial attacks 533   15.12 Conclusions 537   References 538   Index 545","brand":"Elsevier Science","offers":[{"title":"Default Title","offer_id":52083808665943,"sku":"9780128221099","price":86.36,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780128221099.jpg?v=1762203886","url":"https:\/\/bookcurl.com\/products\/advanced-methods-and-deep-learning-in-computer-vision-9780128221099","provider":"Book Curl","version":"1.0","type":"link"}