| Description |
1 online resource (584 p.) |
| Series |
Computer Vision and Pattern Recognition Ser |
|
Computer Vision and Pattern Recognition Ser
|
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
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1.8 Part G -- Summary |
| Notes |
Description based upon print version of record |
| Subject |
Computer vision.
|
|
Pattern recognition systems -- Data processing
|
|
Computer vision
|
|
Pattern recognition systems -- Data processing
|
| Form |
Electronic book
|
| Author |
Turk, Matthew
|
| ISBN |
9780128221495 |
|
0128221496 |
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