%{search_type} search results

1,968 catalog results

RSS feed for this result
Book
1 online resource (xxii, 919 pages) : illustrations,
  • Datasets and Performance Analysis in Early Vision
  • Visual Analysis of Sketches
  • Biological and Artificial Vision
  • Brave New Ideas for Motion Representations
  • Joint Imagenet and MS Coco Visual Recognition Challenge
  • Geometry Meets Deep Learning
  • Action and Anticipation for Visual Learning
  • Computer Vision for Road Scene Understanding and Autonomous Driving
  • Challenge on Automatic Personality Analysis
  • BioImage Computing
  • Benchmarking Multi-Target Tracking: MOTChallenge
  • Assistive Computer Vision and Robotics
  • Transferring and Adapting Source Knowledge in Computer Vision
  • Recovering 6D Object Pose
  • Robust Reading
  • 3D Face Alignment in the Wild and Challenge
  • Egocentric Perception, Interaction and Computing
  • Local Features: State of the Art, Open Problems and Performance Evaluation
  • Crowd Understanding
  • Video Segmentation
  • The Visual Object Tracking Challenge Workshop
  • Web-scale Vision and Social Media
  • Computer Vision for Audio-visual Media
  • Computer VISion for ART Analysis
  • Virtual/Augmented Reality for Visual Artificial Intelligence
  • Joint Workshop on Storytelling with Images and Videos and Large Scale Movie Description and Understanding Challenge.
The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. 27 workshops from 44 workshops proposals were selected for inclusion in the proceedings. These address the following themes: Datasets and Performance Analysis in Early Vision; Visual Analysis of Sketches; Biological and Artificial Vision; Brave New Ideas for Motion Representations; Joint Imagenet and MS Coco Visual Recognition Challenge; Geometry Meets Deep Learning; Action and Anticipation for Visual Learning; Computer Vision for Road Scene Understanding and Autonomous Driving; Challenge on Automatic Personality Analysis; BioImage Computing; Benchmarking Multi-Target Tracking: MOTChallenge; Assistive Computer Vision and Robotics; Transferring and Adapting Source Knowledge in Computer Vision; Recovering 6D Object Pose; Robust Reading; 3D Face Alignment in the Wild and Challenge; Egocentric Perception, Interaction and Computing; Local Features: State of the Art, Open Problems and Performance Evaluation; Crowd Understanding; Video Segmentation; The Visual Object Tracking Challenge Workshop; Web-scale Vision and Social Media; Computer Vision for Audio-visual Media; Computer VISion for ART Analysis; Virtual/Augmented Reality for Visual Artificial Intelligence; Joint Workshop on Storytelling with Images and Videos and Large Scale Movie Description and Understanding Challenge.
Book
1 online resource (xxiii, 922 pages) : illustrations.
  • Datasets and Performance Analysis in Early Vision
  • Visual Analysis of Sketches
  • Biological and Artificial Vision
  • Brave New Ideas for Motion Representations
  • Joint Imagenet and MS Coco Visual Recognition Challenge
  • Geometry Meets Deep Learning
  • Action and Anticipation for Visual Learning
  • Computer Vision for Road Scene Understanding and Autonomous Driving
  • Challenge on Automatic Personality Analysis
  • BioImage Computing
  • Benchmarking Multi-Target Tracking: MOTChallenge
  • Assistive Computer Vision and Robotics
  • Transferring and Adapting Source Knowledge in Computer Vision
  • Recovering 6D Object Pose
  • Robust Reading
  • 3D Face Alignment in the Wild and Challenge
  • Egocentric Perception, Interaction and Computing
  • Local Features: State of the Art, Open Problems and Performance Evaluation
  • Crowd Understanding
  • Video Segmentation
  • The Visual Object Tracking Challenge Workshop
  • Web-scale Vision and Social Media
  • Computer Vision for Audio-visual Media
  • Computer VISion for ART Analysis
  • Virtual/Augmented Reality for Visual Artificial Intelligence
  • Joint Workshop on Storytelling with Images and Videos and Large Scale Movie Description and Understanding Challenge.
The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. 27 workshops from 44 workshops proposals were selected for inclusion in the proceedings. These address the following themes: Datasets and Performance Analysis in Early Vision; Visual Analysis of Sketches; Biological and Artificial Vision; Brave New Ideas for Motion Representations; Joint ImageNet and MS COCO Visual Recognition Challenge; Geometry Meets Deep Learning; Action and Anticipation for Visual Learning; Computer Vision for Road Scene Understanding and Autonomous Driving; Challenge on Automatic Personality Analysis; BioImage Computing; Benchmarking Multi-Target Tracking: MOTChallenge; Assistive Computer Vision and Robotics; Transferring and Adapting Source Knowledge in Computer Vision; Recovering 6D Object Pose; Robust Reading; 3D Face Alignment in the Wild and Challenge; Egocentric Perception, Interaction and Computing; Local Features: State of the Art, Open Problems and Performance Evaluation; Crowd Understanding; Video Segmentation; The Visual Object Tracking Challenge Workshop; Web-scale Vision and Social Media; Computer Vision for Audio-visual Media; Computer VISion for ART Analysis; Virtual/Augmented Reality for Visual Artificial Intelligence; Joint Workshop on Storytelling with Images and Videos and Large Scale Movie Description and Understanding Challenge.
Book
1 online resource (xxiii, 919 pages) : illustrations.
  • Datasets and Performance Analysis in Early Vision
  • Visual Analysis of Sketches
  • Biological and Artificial Vision
  • Brave New Ideas for Motion Representations
  • Joint Imagenet and MS Coco Visual Recognition Challenge
  • Geometry Meets Deep Learning
  • Action and Anticipation for Visual Learning
  • Computer Vision for Road Scene Understanding and Autonomous Driving
  • Challenge on Automatic Personality Analysis
  • BioImage Computing
  • Benchmarking Multi-Target Tracking: MOTChallenge
  • Assistive Computer Vision and Robotics
  • Transferring and Adapting Source Knowledge in Computer Vision
  • Recovering 6D Object Pose
  • Robust Reading
  • 3D Face Alignment in the Wild and Challenge
  • Egocentric Perception, Interaction and Computing
  • Local Features: State of the Art, Open Problems and Performance Evaluation
  • Crowd Understanding
  • Video Segmentation
  • The Visual Object Tracking Challenge Workshop
  • Web-scale Vision and Social Media
  • Computer Vision for Audio-visual Media
  • Computer VISion for ART Analysis
  • Virtual/Augmented Reality for Visual Artificial Intelligence
  • Joint Workshop on Storytelling with Images and Videos and Large Scale Movie Description and Understanding Challenge.
The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. 27 workshops from 44 workshops proposals were selected for inclusion in the proceedings. These address the following themes: Datasets and Performance Analysis in Early Vision; Visual Analysis of Sketches; Biological and Artificial Vision; Brave New Ideas for Motion Representations; Joint ImageNet and MS COCO Visual Recognition Challenge; Geometry Meets Deep Learning; Action and Anticipation for Visual Learning; Computer Vision for Road Scene Understanding and Autonomous Driving; Challenge on Automatic Personality Analysis; BioImage Computing; Benchmarking Multi-Target Tracking: MOTChallenge; Assistive Computer Vision and Robotics; Transferring and Adapting Source Knowledge in Computer Vision; Recovering 6D Object Pose; Robust Reading; 3D Face Alignment in the Wild and Challenge; Egocentric Perception, Interaction and Computing; Local Features: State of the Art, Open Problems and Performance Evaluation; Crowd Understanding; Video Segmentation; The Visual Object Tracking Challenge Workshop; Web-scale Vision and Social Media; Computer Vision for Audio-visual Media; Computer VISion for ART Analysis; Virtual/Augmented Reality for Visual Artificial Intelligence; Joint Workshop on Storytelling with Images and Videos and Large Scale Movie Description and Understanding Challenge.
Book
1 online resource (xxxiv, 898 pages) : illustrations (some color)
  • Over 240 entries organized A to Z.
  • (source: Nielsen Book Data)9780387314396 20160614
This comprehensive reference provides easy access to relevant information on all aspects of Computer Vision. An A-Z format of over 240 entries offers a diverse range of topics for those seeking entry into any aspect within the broad field of Computer Vision. Over 200 Authors from both industry and academia contributed to this volume. Each entry includes synonyms, a definition and discussion of the topic, and a robust bibliography. Extensive cross-references to other entries support efficient, user-friendly searches for immediate access to relevant information. Entries were peer-reviewed by a distinguished international advisory board, both scientifically and geographically diverse, ensuring balanced coverage. Over 3700 bibliographic references for further reading enable deeper exploration into any of the topics covered. The content of Computer Vision: A Reference Guide is expository and tutorial, making the book a practical resource for students who are considering entering the field, as well as professionals in other fields who need to access this vital information but may not have the time to work their way through an entire text on their topic of interest.
(source: Nielsen Book Data)9780387314396 20160614
Book
1 online resource (235 pages) : illustrations, photographs
  • Preface xiii 1 Introduction 1 1.1 A Difficult Problem 1 1.2 The Human Vision System 2 1.3 Practical Applications of Computer Vision 3 1.4 The Future of Computer Vision 5 1.5 Material in This Textbook 6 1.6 Going Further with Computer Vision 7 2 Images 9 2.1 Cameras 9 2.1.1 The Simple Pinhole Camera Model 9 2.2 Images 10 2.2.1 Sampling 11 2.2.2 Quantisation 11 2.3 Colour Images 13 2.3.1 Red Green Blue (RGB) Images 14 2.3.2 Cyan Magenta Yellow (CMY) Images 17 2.3.3 YUV Images 17 2.3.4 Hue Luminance Saturation (HLS) Images 18 2.3.5 Other Colour Spaces 20 2.3.6 Some Colour Applications 20 2.4 Noise 22 2.4.1 Types of Noise 23 2.4.2 Noise Models 25 2.4.3 Noise Generation 26 2.4.4 Noise Evaluation 26 2.5 Smoothing 27 2.5.1 Image Averaging 27 2.5.2 Local Averaging and Gaussian Smoothing 28 2.5.3 Rotating Mask 30 2.5.4 Median Filter 31 3 Histograms 35 3.1 1D Histograms 35 3.1.1 Histogram Smoothing 36 3.1.2 Colour Histograms 37 3.2 3D Histograms 39 3.3 Histogram/Image Equalisation 40 3.4 Histogram Comparison 41 3.5 Back-projection 43 3.6 k-means Clustering 44 4 Binary Vision 49 4.1 Thresholding 49 4.1.1 Thresholding Problems 50 4.2 Threshold Detection Methods 51 4.2.1 Bimodal Histogram Analysis 52 4.2.2 Optimal Thresholding 52 4.2.3 Otsu Thresholding 54 4.3 Variations on Thresholding 56 4.3.1 Adaptive Thresholding 56 4.3.2 Band Thresholding 57 4.3.3 Semi-thresholding 58 4.3.4 Multispectral Thresholding 58 4.4 Mathematical Morphology 59 4.4.1 Dilation 60 4.4.2 Erosion 62 4.4.3 Opening and Closing 63 4.4.4 Grey-scale and Colour Morphology 65 4.5 Connectivity 66 4.5.1 Connectedness: Paradoxes and Solutions 66 4.5.2 Connected Components Analysis 67 5 Geometric Transformations 71 5.1 Problem Specification and Algorithm 71 5.2 Affine Transformations 73 5.2.1 Known Affine Transformations 74 5.2.2 Unknown Affine Transformations 75 5.3 Perspective Transformations 76 5.4 Specification of More Complex Transformations 78 5.5 Interpolation 78 5.5.1 Nearest Neighbour Interpolation 79 5.5.2 Bilinear Interpolation 79 5.5.3 Bi-Cubic Interpolation 80 5.6 Modelling and Removing Distortion from Cameras 80 5.6.1 Camera Distortions 81 5.6.2 Camera Calibration and Removing Distortion 82 6 Edges 83 6.1 Edge Detection 83 6.1.1 First Derivative Edge Detectors 85 6.1.2 Second Derivative Edge Detectors 92 6.1.3 Multispectral Edge Detection 97 6.1.4 Image Sharpening 98 6.2 Contour Segmentation 99 6.2.1 Basic Representations of Edge Data 99 6.2.2 Border Detection 102 6.2.3 Extracting Line Segment Representations of Edge Contours 105 6.3 Hough Transform 108 6.3.1 Hough for Lines 109 6.3.2 Hough for Circles 111 6.3.3 Generalised Hough 112 7 Features 115 7.1 Moravec Corner Detection 117 7.2 Harris Corner Detection 118 7.3 FAST Corner Detection 121 7.4 SIFT 122 7.4.1 Scale Space Extrema Detection 123 7.4.2 Accurate Keypoint Location 124 7.4.3 Keypoint Orientation Assignment 126 7.4.4 Keypoint Descriptor 127 7.4.5 Matching Keypoints 127 7.4.6 Recognition 127 7.5 Other Detectors 129 7.5.1 Minimum Eigenvalues 130 7.5.2 SURF 130 8 Recognition 131 8.1 Template Matching 131 8.1.1 Applications 131 8.1.2 Template Matching Algorithm 133 8.1.3 Matching Metrics 134 8.1.4 Finding Local Maxima or Minima 135 8.1.5 Control Strategies for Matching 137 8.2 Chamfer Matching 137 8.2.1 Chamfering Algorithm 137 8.2.2 Chamfer Matching Algorithm 139 8.3 Statistical Pattern Recognition 140 8.3.1 Probability Review 142 8.3.2 Sample Features 143 8.3.3 Statistical Pattern Recognition Technique 149 8.4 Cascade of Haar Classifiers 152 8.4.1 Features 154 8.4.2 Training 156 8.4.3 Classifiers 156 8.4.4 Recognition 158 8.5 Other Recognition Techniques 158 8.5.1 Support Vector Machines (SVM) 158 8.5.2 Histogram of Oriented Gradients (HoG) 159 8.6 Performance 160 8.6.1 Image and Video Datasets 160 8.6.2 Ground Truth 161 8.6.3 Metrics for Assessing Classification Performance 162 8.6.4 Improving Computation Time 165 9 Video 167 9.1 Moving Object Detection 167 9.1.1 Object of Interest 168 9.1.2 Common Problems 168 9.1.3 Difference Images 169 9.1.4 Background Models 171 9.1.5 Shadow Detection 179 9.2 Tracking 180 9.2.1 Exhaustive Search 181 9.2.2 Mean Shift 181 9.2.3 Dense Optical Flow 182 9.2.4 Feature Based Optical Flow 185 9.3 Performance 186 9.3.1 Video Datasets (and Formats) 186 9.3.2 Metrics for Assessing Video Tracking Performance 187 10 Vision Problems 189 10.1 Baby Food 189 10.2 Labels on Glue 190 10.3 O-rings 191 10.4 Staying in Lane 192 10.5 Reading Notices 193 10.6 Mailboxes 194 10.7 Abandoned and Removed Object Detection 195 10.8 Surveillance 196 10.9 Traffic Lights 197 10.10 Real Time Face Tracking 198 10.11 Playing Pool 199 10.12 Open Windows 200 10.13 Modelling Doors 201 10.14 Determining the Time from Analogue Clocks 202 10.15 Which Page 203 10.16 Nut/Bolt/Washer Classification 204 10.17 Road Sign Recognition 205 10.18 License Plates 206 10.19 Counting Bicycles 207 10.20 Recognise Paintings 208 References 209 Index 213.
  • (source: Nielsen Book Data)9781118848456 20160802
Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries Computer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the ready availability of high quality libraries (such as OpenCV 2). This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the relevant OpenCV library routines and will be accompanied by a full working program including the code snippets from the text. This textbook is a heavily illustrated, practical introduction to an exciting field, the applications of which are becoming almost ubiquitous. We are now surrounded by cameras, for example cameras on computers & tablets/ cameras built into our mobile phones/ cameras in games consoles; cameras imaging difficult modalities (such as ultrasound, X-ray, MRI) in hospitals, and surveillance cameras. This book is concerned with helping the next generation of computer developers to make use of all these images in order to develop systems which are more intuitive and interact with us in more intelligent ways. * Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries * Offers an introduction to computer vision, with enough theory to make clear how the various algorithms work but with an emphasis on practical programming issues * Provides enough material for a one semester course in computer vision at senior undergraduate and Masters levels * Includes the basics of cameras and images and image processing to remove noise, before moving on to topics such as image histogramming; binary imaging; video processing to detect and model moving objects; geometric operations & camera models; edge detection; features detection; recognition in images * Contains a large number of vision application problems to provide students with the opportunity to solve real problems. Images or videos for these problems are provided in the resources associated with this book which include an enhanced eBook.
(source: Nielsen Book Data)9781118848456 20160802
Book
1 online resource (1 volume) : illustrations
  • Preface xiii 1 Introduction 1 1.1 A Difficult Problem 1 1.2 The Human Vision System 2 1.3 Practical Applications of Computer Vision 3 1.4 The Future of Computer Vision 5 1.5 Material in This Textbook 6 1.6 Going Further with Computer Vision 7 2 Images 9 2.1 Cameras 9 2.1.1 The Simple Pinhole Camera Model 9 2.2 Images 10 2.2.1 Sampling 11 2.2.2 Quantisation 11 2.3 Colour Images 13 2.3.1 Red Green Blue (RGB) Images 14 2.3.2 Cyan Magenta Yellow (CMY) Images 17 2.3.3 YUV Images 17 2.3.4 Hue Luminance Saturation (HLS) Images 18 2.3.5 Other Colour Spaces 20 2.3.6 Some Colour Applications 20 2.4 Noise 22 2.4.1 Types of Noise 23 2.4.2 Noise Models 25 2.4.3 Noise Generation 26 2.4.4 Noise Evaluation 26 2.5 Smoothing 27 2.5.1 Image Averaging 27 2.5.2 Local Averaging and Gaussian Smoothing 28 2.5.3 Rotating Mask 30 2.5.4 Median Filter 31 3 Histograms 35 3.1 1D Histograms 35 3.1.1 Histogram Smoothing 36 3.1.2 Colour Histograms 37 3.2 3D Histograms 39 3.3 Histogram/Image Equalisation 40 3.4 Histogram Comparison 41 3.5 Back-projection 43 3.6 k-means Clustering 44 4 Binary Vision 49 4.1 Thresholding 49 4.1.1 Thresholding Problems 50 4.2 Threshold Detection Methods 51 4.2.1 Bimodal Histogram Analysis 52 4.2.2 Optimal Thresholding 52 4.2.3 Otsu Thresholding 54 4.3 Variations on Thresholding 56 4.3.1 Adaptive Thresholding 56 4.3.2 Band Thresholding 57 4.3.3 Semi-thresholding 58 4.3.4 Multispectral Thresholding 58 4.4 Mathematical Morphology 59 4.4.1 Dilation 60 4.4.2 Erosion 62 4.4.3 Opening and Closing 63 4.4.4 Grey-scale and Colour Morphology 65 4.5 Connectivity 66 4.5.1 Connectedness: Paradoxes and Solutions 66 4.5.2 Connected Components Analysis 67 5 Geometric Transformations 71 5.1 Problem Specification and Algorithm 71 5.2 Affine Transformations 73 5.2.1 Known Affine Transformations 74 5.2.2 Unknown Affine Transformations 75 5.3 Perspective Transformations 76 5.4 Specification of More Complex Transformations 78 5.5 Interpolation 78 5.5.1 Nearest Neighbour Interpolation 79 5.5.2 Bilinear Interpolation 79 5.5.3 Bi-Cubic Interpolation 80 5.6 Modelling and Removing Distortion from Cameras 80 5.6.1 Camera Distortions 81 5.6.2 Camera Calibration and Removing Distortion 82 6 Edges 83 6.1 Edge Detection 83 6.1.1 First Derivative Edge Detectors 85 6.1.2 Second Derivative Edge Detectors 92 6.1.3 Multispectral Edge Detection 97 6.1.4 Image Sharpening 98 6.2 Contour Segmentation 99 6.2.1 Basic Representations of Edge Data 99 6.2.2 Border Detection 102 6.2.3 Extracting Line Segment Representations of Edge Contours 105 6.3 Hough Transform 108 6.3.1 Hough for Lines 109 6.3.2 Hough for Circles 111 6.3.3 Generalised Hough 112 7 Features 115 7.1 Moravec Corner Detection 117 7.2 Harris Corner Detection 118 7.3 FAST Corner Detection 121 7.4 SIFT 122 7.4.1 Scale Space Extrema Detection 123 7.4.2 Accurate Keypoint Location 124 7.4.3 Keypoint Orientation Assignment 126 7.4.4 Keypoint Descriptor 127 7.4.5 Matching Keypoints 127 7.4.6 Recognition 127 7.5 Other Detectors 129 7.5.1 Minimum Eigenvalues 130 7.5.2 SURF 130 8 Recognition 131 8.1 Template Matching 131 8.1.1 Applications 131 8.1.2 Template Matching Algorithm 133 8.1.3 Matching Metrics 134 8.1.4 Finding Local Maxima or Minima 135 8.1.5 Control Strategies for Matching 137 8.2 Chamfer Matching 137 8.2.1 Chamfering Algorithm 137 8.2.2 Chamfer Matching Algorithm 139 8.3 Statistical Pattern Recognition 140 8.3.1 Probability Review 142 8.3.2 Sample Features 143 8.3.3 Statistical Pattern Recognition Technique 149 8.4 Cascade of Haar Classifiers 152 8.4.1 Features 154 8.4.2 Training 156 8.4.3 Classifiers 156 8.4.4 Recognition 158 8.5 Other Recognition Techniques 158 8.5.1 Support Vector Machines (SVM) 158 8.5.2 Histogram of Oriented Gradients (HoG) 159 8.6 Performance 160 8.6.1 Image and Video Datasets 160 8.6.2 Ground Truth 161 8.6.3 Metrics for Assessing Classification Performance 162 8.6.4 Improving Computation Time 165 9 Video 167 9.1 Moving Object Detection 167 9.1.1 Object of Interest 168 9.1.2 Common Problems 168 9.1.3 Difference Images 169 9.1.4 Background Models 171 9.1.5 Shadow Detection 179 9.2 Tracking 180 9.2.1 Exhaustive Search 181 9.2.2 Mean Shift 181 9.2.3 Dense Optical Flow 182 9.2.4 Feature Based Optical Flow 185 9.3 Performance 186 9.3.1 Video Datasets (and Formats) 186 9.3.2 Metrics for Assessing Video Tracking Performance 187 10 Vision Problems 189 10.1 Baby Food 189 10.2 Labels on Glue 190 10.3 O-rings 191 10.4 Staying in Lane 192 10.5 Reading Notices 193 10.6 Mailboxes 194 10.7 Abandoned and Removed Object Detection 195 10.8 Surveillance 196 10.9 Traffic Lights 197 10.10 Real Time Face Tracking 198 10.11 Playing Pool 199 10.12 Open Windows 200 10.13 Modelling Doors 201 10.14 Determining the Time from Analogue Clocks 202 10.15 Which Page 203 10.16 Nut/Bolt/Washer Classification 204 10.17 Road Sign Recognition 205 10.18 License Plates 206 10.19 Counting Bicycles 207 10.20 Recognise Paintings 208 References 209 Index 213.
  • (source: Nielsen Book Data)9781118848456 20160711
Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries Computer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the ready availability of high quality libraries (such as OpenCV 2). This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the relevant OpenCV library routines and will be accompanied by a full working program including the code snippets from the text. This textbook is a heavily illustrated, practical introduction to an exciting field, the applications of which are becoming almost ubiquitous. We are now surrounded by cameras, for example cameras on computers & tablets/ cameras built into our mobile phones/ cameras in games consoles; cameras imaging difficult modalities (such as ultrasound, X-ray, MRI) in hospitals, and surveillance cameras. This book is concerned with helping the next generation of computer developers to make use of all these images in order to develop systems which are more intuitive and interact with us in more intelligent ways. * Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries * Offers an introduction to computer vision, with enough theory to make clear how the various algorithms work but with an emphasis on practical programming issues * Provides enough material for a one semester course in computer vision at senior undergraduate and Masters levels * Includes the basics of cameras and images and image processing to remove noise, before moving on to topics such as image histogramming; binary imaging; video processing to detect and model moving objects; geometric operations & camera models; edge detection; features detection; recognition in images * Contains a large number of vision application problems to provide students with the opportunity to solve real problems. Images or videos for these problems are provided in the resources associated with this book which include an enhanced eBook.
(source: Nielsen Book Data)9781118848456 20160711
Book
xxv, 693 p. : ill. ; 26 cm.
  • I. IMAGE FORMATION AND IMAGE MODELS. 1. Cameras. 2. Geometric Camera Models. 3. Geometric Camera Calibration. 4. Radiometry - Measuring Light. 5. Sources, Shadows and Shading. 6. Color. II. EARLY VISION: JUST ONE IMAGE. 7. Linear Filters. 8. Edge Detection. 9. Texture. III. EARLY VISION: MULTIPLE IMAGES. 10. The Geometry of Multiple Views. 11. Stereopsis. 12. Affine Structure from Motion. 13. Projective Structure from Motion. IV. MID-LEVEL VISION. 14. Segmentation By Clustering. 15. Segmentation By Fitting a Model. 16. Segmentation and Fitting Using Probabilistic Methods. 17. Tracking with Linear Dynamic Models. V. HIGH-LEVEL VISION: GEOMETRIC MODELS. 18. Model-Based Vision. 19. Smooth Surfaces and Their Outlines. 20. Aspect Graphs. 21. Range Data. VI. HIGH-LEVEL VISION: PROBABILISTIC AND INFERENTIAL METHODS. 22. Finding Templates Using Classifiers. 23. Recognition By Relations Between Templates. 24. Geometric Templates From Spatial Relations. VII. APPLICATIONS. 25. Application: Finding in Digital Libraries. 26. Application: Image-Based Rendering.
  • (source: Nielsen Book Data)9780130851987 20160528
Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This long anticipated book is the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.
(source: Nielsen Book Data)9780130851987 20160528
Engineering Library (Terman)

12. Videre [1997 - 1999]

Book
1 online resource : illustrations (some color).
Book
xxix, 761 pages : ill. ; 26 cm
  • 1. Geometric Camera Models
  • 2. Light and Shading
  • 3. Color
  • 4. Linear Filters
  • 5. Local Image Features
  • 6. Texture
  • 7. Stereopsis
  • 8. Structure from Motion
  • 9. Segmentation by Clustering
  • 10. Grouping and Model Fitting
  • 11. Tracking
  • 12. Registration
  • 13. Smooth Surfaces and Their Outlines
  • 14. Range Data
  • 15. Learning to Classify
  • 16. Classifying Images
  • 17. Detecting Objects in Images
  • 18. Topics in Object Recognition
  • 19. Image -Based Modeling and Rendering
  • 20. Looking at People
  • 21. Image Search and Retrieval
  • 22. Optimization Techniques.
Computer Vision: A Modern Approach, 2e, is appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.
(source: Nielsen Book Data)9780136085928 20160618
Engineering Library (Terman)
Book
1 online resource (xviii, 637 pages) : illustrations (some color)
Book
1 online resource (xii, 99 pages) : illustrations (some color).
This book covers a wide range of local image descriptors, from the classical ones to the state of the art, as well as the burgeoning research topics on this area. The goal of this effort is to let readers know what are the most popular and useful methods in the current, what are the advantages and the disadvantages of these methods, which kind of methods is best suitable for their problems or applications, and what is the future of this area. What is more, hands-on exemplars supplied in this book will be of great interest to Computer Vision engineers and practitioners, as well as those want to begin their research in this area. Overall, this book is suitable for graduates, researchers and engineers in the related areas both as a learning text and as a reference book.
Book
1 online resource (1 v.) : ill.
  • Chapter 1. Image Capture and Representation Chapter 2. Image Pre-Processing Chapter 3. Global and Regional Features Chapter 4. Local Feature Design Concepts, Classification, and Learning Chapter 5. Taxonomy Of Feature Description Attributes Chapter 6. Interest Point Detector and Feature Descriptor Survey Chapter 7. Ground Truth Data, Data, Metrics, and Analysis Chapter 8. Vision Pipelines and Optimizations Appendix A. Synthetic Feature Analysis Appendix B. Survey of Ground Truth Datasets Appendix C. Imaging and Computer Vision Resources Appendix D. Extended SDM Metrics.
  • (source: Nielsen Book Data)9781430259299 20160711
Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing 'how-to' source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners.
(source: Nielsen Book Data)9781430259299 20160711

Articles+

Journal articles, e-books, & other e-resources
Articles+ results include