A practical introduction to computer vision with OpenCV /

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"Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV librariesComputer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Dawson-Howe, Kenneth
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Chichester, West Sussex, United Kingdon ; Hoboken, N.J. : John Wiley & Sons, 2014.
Temas:
Computer vision.
Computer vision > Computer programs.
Vision par ordinateur.
Vision par ordinateur > Logiciels.
COMPUTERS > Computer Vision & Pattern Recognition.
Computer vision
Computer vision > Computer programs
Engineering & Applied Sciences.
Applied Physics.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Machine generated contents note: 1. Introduction
  • 1.1. A Difficult Problem
  • 1.2. The Human Vision System
  • 1.3. Practical Applications of Computer Vision
  • 1.4. The Future of Computer Vision
  • 1.5. Material in This Textbook
  • 1.6. Going Further with Computer Vision
  • 2. Images
  • 2.1. Cameras
  • 2.1.1. The Simple Pinhole Camera Model
  • 2.2. Images
  • 2.2.1. Sampling
  • 2.2.2. Quantisation
  • 2.3. Colour Images
  • 2.3.1. Red-Green
  • Blue (RGB) Images
  • 2.5.2. Cyan-Magenta
  • Yellow (CMY) Images
  • 2.5.3. YUV Images
  • 2.5.4. Hue Luminance Saturation (HLS) Images
  • 2.5.5. Other Colour Spaces
  • 2.5.6. Some Colour Applications
  • 2.4. Noise
  • 2.4.1. Types of Noise
  • 2.4.2. Noise Models
  • 2.4.3. Noise Generation
  • 2.4.4. Noise Evaluation
  • 2.5. Smoothing
  • 2.5.1. Image Averaging
  • 2.5.2. Local Averaging and Gaussian Smoothing
  • 2.5.3. Rotating Mask
  • 2.5.4. Median Filter
  • 3. Histograms
  • 3.1. 1D Histograms
  • 3.1.1. Histogram Smoothing
  • 3.1.2. Colour Histograms
  • 3.2. 3D Histograms
  • 3.3. Histogram/Image Equalisation
  • 3.4. Histogram Comparison
  • 3.5. Back-projection
  • 3.6. k-means Clustering
  • 4. Binary Vision
  • 4.1. Thresholding
  • 4.1.1. Thresholding Problems
  • 4.2. Threshold Detection Methods
  • 4.2.1. Bimodal Histogram Analysis
  • 4.2.2. Optimal Thresholding
  • 4.2.3. Otsu Thresholding
  • 4.3. Variations on Thresholding
  • 4.3.1. Adaptive Thresholding
  • 4.3.2. Band Thresholding
  • 4.3.3. Semi-thresholding
  • 4.3.4. Multispectral Thresholding
  • 4.4. Mathematical Morphology
  • 4.4.1. Dilation
  • 4.4.2. Erosion
  • 4.4.3. Opening and Closing
  • 4.4.4. Grey-scale and Colour Morphology
  • 4.5. Connectivity
  • 4.5.1. Connectedness: Paradoxes and Solutions
  • 4.5.2. Connected Components Analysis
  • 5. Geometric Transformations
  • 5.1. Problem Specification and Algorithm
  • 5.2. Affine Transformations
  • 5.2.1. Known Affine Transformations
  • 5.2.2. Unknown Affine Transformations
  • 5.3. Perspective Transformations
  • 5.4. Specification of More Complex Transformations
  • 5.5. Interpolation
  • 5.5.1. Nearest Neighbour Interpolation
  • 5.5.2. Bilinear Interpolation
  • 5.5.3. Bi-Cubic Interpolation
  • 5.6. Modelling and Removing Distortion from Cameras
  • 5.6.7. Camera Distortions
  • 5.6.2. Camera Calibration and Removing Distortion
  • 6. Edges
  • 6.1. Edge Detection
  • 6.1.1. First Derivative Edge Detectors
  • 6.1.2. Second Derivative Edge Detectors
  • 6.1.3. Multispectral Edge Detection
  • 6.1.4. Image Sharpening
  • 6.2. Contour Segmentation
  • 6.2.1. Basic Representations of Edge Data
  • 6.2.2. Border Detection
  • 6.2.3. Extracting Line Segment Representations of Edge Contours
  • 6.3. Hough Transform
  • 6.3.1. Hough for Lines
  • 6.3.2. Hough for Circles
  • 6.3.3. Generalised Hough
  • 7. Features
  • 7.1. Moravec Corner Detection
  • 7.2. Harris Corner Detection
  • 7.3. FAST Corner Detection
  • 7.4. SIFT
  • 7.4.1. Scale Space Extrema Detection
  • 7.4.2. Accurate Keypoint Location
  • 7.4.3. Keypoint Orientation Assignment
  • 7.4.4. Keypoint Descriptor
  • 7.4.5. Matching Keypoints
  • 7.4.6. Recognition
  • 7.5. Other Detectors
  • 7.5.1. Minimum Eigenvalues
  • 7.5.2. SURF
  • 8. Recognition
  • 8.1. Template Matching
  • 8.1.1. Applications
  • 8.1.2. Template Matching Algorithm
  • 8.1.3. Matching Metrics
  • 8.1.4. Finding Local Maxima or Minima
  • 8.1.5. Control Strategies for Matching
  • 8.2. Chamfer Matching
  • 8.2.1. Chamfering Algorithm
  • 8.2.2. Chamfer Matching Algorithm
  • 8.3. Statistical Pattern Recognition
  • 8.3.1. Probability Review
  • 8.3.2. Sample Features
  • 8.3.3. Statistical Pattern Recognition Technique
  • 8.4. Cascade of Haar Classifiers
  • 8.4.1. Features
  • 8.4.2. Training
  • 8.4.3. Classifiers
  • 8.4.4. Recognition
  • 8.5. Other Recognition Techniques
  • 8.5.1. Support Vector Machines (SVM)
  • 8.5.2. Histogram of Oriented Gradients (HoG)
  • 8.6. Performance
  • 8.6.1. Image and Video Datasets
  • 8.6.2. Ground Truth
  • 8.6.3. Metrics for Assessing Classification Performance
  • 8.6.4. Improving Computation Time
  • 9. Video
  • 9.1. Moving Object Detection
  • 9.1.1. Object of Interest
  • 9.1.2. Common Problems
  • 9.1.3. Difference Images
  • 9.1.4. Background Models
  • 9.1.5. Shadow Detection
  • 9.2. Tracking
  • 9.2.1. Exhaustive Search
  • 9.2.2. Mean Shift
  • 9.2.3. Dense Optical Flow
  • 9.2.4. Feature Based Optical Flow
  • 9.3. Performance
  • 9.3.1. Video Datasets (and Formats)
  • 9.3.2. Metrics for Assessing Video Tracking Performance
  • 10. Vision Problems
  • 10.1. Baby Food
  • 10.2. Labels on Glue
  • 10.3. O-rings
  • 10.4. Staying in Lane
  • 10.5. Reading Notices
  • 10.6. Mailboxes
  • 10.7. Abandoned and Removed Object Detection
  • 10.8. Surveillance
  • 10.9. Traffic Lights
  • 10.10. Real Time Face Tracking
  • 10.11. Playing Pool
  • 10.12. Open Windows
  • 10.13. Modelling Doors
  • 10.14. Determining the Time from Analogue Clocks
  • 10.15. Which Page
  • 10.16. Nut/Bolt/Washer Classification
  • 10.17. Road Sign Recognition
  • 10.18. License Plates
  • 10.19. Counting Bicycles
  • 10.20. Recognise Paintings.

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