Learning OpenCV 3 Application Development.

Learning OpenCV 3 Application Development.

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Bibliographic Details
Main Author: Datta, Samyak
Format: eBook
Language:English
Published: Birmingham : Packt Publishing, 2016.
Subjects:
Application software.
OpenCV.
Computers > Desktop Applications > General.
Application software
Online Access:Click for online access
Table of Contents:
  • Cover; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Laying the Foundation; Digital image basics; Pixel intensities; Color depth and color spaces; Color channels; Introduction to the Mat class; Exploring the Mat class: loading images; Exploring the Mat class
  • declaring Mat objects; Spatial dimensions of an image; Color space or color depth; Color channels; Image size; Default initialization value; Digging inside Mat objects; Traversing Mat objects; Continuity of the Mat data matrix; Image traversals; Image enhancement; Lookup tables.
  • Linear transformationsIdentity transformation; Negative transformation; Logarithmic transformations; Log transformation; Exponential or inverse-log transformation; Summary; Chapter 2: Image Filtering; Neighborhood of a pixel; Image averaging; Image filters; Image averaging in OpenCV; Blurring an image in OpenCV; Gaussian smoothing; Gaussian function and Gaussian filtering; Gaussian filtering in OpenCV; Using your own filters in OpenCV; Image noise; Vignetting; Implementing Vignetting in OpenCV; Summary; Chapter 3: Image Thresholding; Binary images; Image thresholding basics.
  • Image thresholding in OpenCVTypes of simple image thresholding; Binary threshold; Inverted binary threshold; Truncate; Threshold-to-zero; Inverted threshold-to-zero; Adaptive thresholding; Morphological operations; Erosion and dilation; Erosion and dilation in OpenCV; Summary; Chapter 4: Image Histograms; The basics of histograms; Histograms in OpenCV; Plotting histograms in OpenCV; Color histograms in OpenCV; Multidimensional histograms in OpenCV; Summary; Chapter 5: Image Derivatives and Edge Detection; Image derivatives; Image derivatives in two dimensions.
  • Visualizing image derivatives with OpenCVThe Sobel derivative filter; From derivatives to edges; The Sobel detector
  • a basic framework for edge detection; The Canny edge detector; Image noise and edge detection; Laplacian
  • yet another edge detection technique; Blur detection using OpenCV; Summary; Chapter 6: Face Detection Using OpenCV; Image classification systems; Face detection; Haar features; Integral image; Integral image in OpenCV; AdaBoost learning; Cascaded classifiers; Face detection in OpenCV; Controlling the quality of detected faces; Gender classification.
  • Working with real datasetsSummary; Chapter 7: Affine Transformations and Face Alignment; Exploring the dataset; Running face detection on the dataset; Face alignment
  • the first step in facial analysis; Rotating faces; Image cropping
  • basics; Image cropping for face alignment; Face alignment
  • the complete pipeline; Summary; Chapter 8: Feature Descriptors in OpenCV; Introduction to the local binary pattern; A basic implementation of LBP; Variants of LBP; What does LBP capture?; Applying LBP to aligned facial images; A complete implementation of LBP.
  • Putting it all together
  • the main() function.

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