Kooperativer Bibliotheksverbund

UID:

Format: XXIV, 547 S. : Ill., graph. Darst.

ISBN: 0-12-206090-3

Series Statement: Microelectronics and signal processing 9

Note: Literaturangaben

Language: English

Subjects: Computer Science

Keywords: Maschinelles Sehen

UID:

Format: 1 online resource (973 p.)

Edition: 3rd ed.

ISBN: 1-281-22725-0 , 9786611227258 , 0-08-047324-5

Series Statement: Signal Processing and its Applications

Content: In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directl

Note: Previous ed.: 1996. , Front Cover; Machine Vision: Theory, Algorithms, Practicalities; Copyright Page; Contents; Foreword; Preface; Acknowledgments; CHAPTER 1. Vision, the Challenge; 1.1 Introduction-The Senses; 1.2 The Nature of Vision; 1.3 From Automated Visual Inspection to Surveillance; 1.4 What This Book Is About; 1.5 The Following Chapters; 1.6 Bibliographical Notes; PART 1: Low-Level Vision; CHAPTER 2. Images and Imaging Operations; 2.1 Introduction; 2.3 Convolutions and Point Spread Functions; 2.4 Sequential versus Parallel Operations; 2.5 Concluding Remarks; 2.6 Bibliographical and Historical Notes , 2.7 ProblemsCHAPTER 3. Basic Image Filtering Operations; 3.1 Introduction; 3.2 Noise Suppression by Gaussian Smoothing; 3.3 Median Filters; 3.4 Mode Filters; 3.5 Rank Order Filters; 3.6 Reducing Computational Load; 3.7 Sharp-Unsharp Masking; 3.8 Shifts Introduced by Median Filters; 3.9 Discrete Model of Median Shifts; 3.10 Shifts Introduced by Mode Filters; 3.11 Shifts Introduced by Mean and Gaussian Filters; 3.12 Shifts Introduced by Rank Order Filters; 3.13 The Role of Filters in Industrial Applications of Vision; 3.14 Color in Image Filtering; 3.15 Concluding Remarks , 3.16 Bibliographical and Historical Notes3.17 Problems; CHAPTER 4. Thresholding Techniques; 4.1 Introduction; 4.2 Region-growing Methods; 4.3 Thresholding; 4.4 Adaptive Thresholding; 4.5 More Thoroughgoing Approaches to Threshold Selection; 4.6 Concluding Remarks; 4.7 Bibliographical and Historical Notes; 4.8 Problems; CHAPTER 5. Edge Detection; 5.1 Introduction; 5.2 Basic Theory of Edge Detection; 5.3 The Template Matching Approach; 5.4 Theory of 3 X 3 Template Operators; 5.5 Summary-Design Constraints and Conclusions; 5.6 The Design of Differential Gradient Operators , 5.7 The Concept of a Circular Operator5.8 Detailed Implementation of Circular Operators; 5.9 Structured Bands of Pixels in Neighborhoods of Various Sizes; 5.10 The Systematic Design of Differential Edge Operators; 5.11 Problems with the above Approach-Some Alternative Schemes; 5.12 Concluding Remarks; 5.13 Bibliographical and Historical Notes; 5.14 Problems; CHAPTER 6. Binary Shape Analysis; 6.1 Introduction; 6.2 Connectedness in Binary Images; 6.3 Object Labeling and Counting; 6.4 Metric Properties in Digital Images; 6.5 Size Filtering; 6.6 The Convex Hull and Its Computation , 6.7 Distance Functions and Their Uses6.8 Skeletons and Thinning; 6.9 Some Simple Measures for Shape Recognition; 6.10 Shape Description by Moments; 6.11 Boundary Tracking Procedures; 6.12 More Detail on the Sigma and Chi Functions; 6.13 Concluding Remarks; 6.14 Bibliographical and Historical Notes; 6.15 Problems; CHAPTER 7. Boundary Pattern Analysis; 7.1 Introduction; 7.2 Boundary Tracking Procedures; 7.3 Template Matching-A Reminder; 7.4 Centroidal Profiles; 7.5 Problems with the Centroidal Profile Approach; 7.6 The (s,y ) Plot; 7.7 Tackling the Problems of Occlusion; 7.8 Chain Code , 7.9 The (r, s) Plot , English

Additional Edition: ISBN 0-12-206093-8

Language: English

UID:

Format: 1 online resource (584 pages)

ISBN: 0-12-822149-6

Series Statement: Computer Vision and Pattern Recognition

Content: "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."--

Note: 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.

Additional Edition: ISBN 0-12-822109-7

Language: English

UID:

Format: 1 online resource (xlii, 858 Seiten) : , Illustrationen, Diagramme.

Edition: Fifth edition

ISBN: 978-0-12-809284-2 , 978-0-12-809575-1 , 0-12-809575-X

Note: Includes bibliographical references and index

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9780128092842

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 012809284X

Language: English

Subjects: Computer Science , Engineering

Keywords: Maschinelles Sehen

DOI: 10.1016/C2015-0-05563-0

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XXXI, 750 S. , Ill., graph. Darst.

Edition: 2. ed.

ISBN: 012206092X

Series Statement: Signal processing and its applications

Note: Literaturangaben

Language: English

Subjects: Computer Science

Keywords: Maschinelles Sehen

URL: Inhaltsverzeichnis

URL: Inhaltsverzeichnis

UID:

Format: xix, 562 Seiten , Illustrationen, Diagramme

ISBN: 9780128221099

Note: Literaturangaben

Language: English

Keywords: Computervision ; Mustererkennung ; Maschinelles Lernen ; Deep learning

URL: Inhaltsverzeichnis

UID:

Format: XXXVI, 871 S. , Ill., graph. Darst

Edition: 4. ed.

ISBN: 9780123869081

Content: Low-level vision -- Intermediate-level vision -- 3-D vision and motion -- Toward real-time pattern recognition systems

Note: Literaturverz. S. 796 - 844 , Low-level vision -- Intermediate-level vision -- 3-D vision and motion -- Toward real-time pattern recognition systems.

Additional Edition: Erscheint auch als Online-Ausgabe Davies, E. R. Computer and machine vision Amsterdam [u.a.] : Elsevier, Acad. Press, 2012 ISBN 9780123869913

Additional Edition: ISBN 0123869080

Additional Edition: ISBN 9780123869081

Additional Edition: Erscheint auch als Online-Ausgabe Computer and machine vision Waltham, Mass : Elsevier, 2012 ISBN 9780123869081

Additional Edition: ISBN 0123869080

Additional Edition: ISBN 9780123869913

Additional Edition: ISBN 0123869919

Former: Bis 3. Aufl. u.d.T. Davies, E. R.: Machine vision

Language: English

Subjects: Computer Science

Keywords: Maschinelles Sehen ; Lehrbuch

URL: Inhaltsverzeichnis

URL: Verlagsangaben

UID:

Format: 1 online resource (xlii, 858 Seiten) : , Illustrationen, Diagramme.

Edition: Fifth edition

ISBN: 978-0-12-809284-2 , 978-0-12-809575-1 , 0-12-809575-X

Note: Includes bibliographical references and index

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9780128092842

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 012809284X

Language: English

Subjects: Computer Science , Engineering

Keywords: Maschinelles Sehen

DOI: 10.1016/C2015-0-05563-0

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (xlii, 858 Seiten) : , Illustrationen, Diagramme.

Edition: Fifth edition

ISBN: 978-0-12-809284-2 , 978-0-12-809575-1 , 0-12-809575-X

Note: Includes bibliographical references and index

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9780128092842

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 012809284X

Language: English

Subjects: Computer Science , Engineering

Keywords: Maschinelles Sehen

DOI: 10.1016/C2015-0-05563-0

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: X, 201 S.

Edition: Reprint.

ISBN: 0435695827

Series Statement: H.E.L.P. publications.

Language: English

Subjects: Education

Keywords: Fallstudiensammlung