Sale!

Learning OpenCV Computer Vision with the OpenCV Library 1st Edition by Adrian Kaehler, Gary Bradski ISBN 1449314651 9781449314651

Original price was: $50.00.Current price is: $35.00.

Learning OpenCV 2nd Edition Computer Vision in C with the OpenCV Library Adrian Kaehler Digital Instant Download

Author(s): Adrian Kaehler, Gary Bradski
ISBN(s): 9781449314651, 1449314651
File Details: PDF, 21.16 MB
Year: 2013
Language: english
SKU: EB-5474200 Category: Tags: ,

Learning OpenCV Computer Vision with the OpenCV Library 1st Edition by Adrian Kaehler, Gary Bradski – Ebook PDF Instant Download/Delivery: 1449314651, 9781449314651
Full download Learning OpenCV Computer Vision with the OpenCV Library 1st Edition after payment

Product details:

ISBN 10: 1449314651 
ISBN 13: 9781449314651
Author: Adrian Kaehler, Gary Bradski

“This library is useful for practitioners, and is an excellent tool for those entering the field: it is a set of computer vision algorithms that work as advertised.”-William T. Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to “see” and make decisions based on that data. Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time. Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes: A thorough introduction to OpenCV Getting input from cameras Transforming images Segmenting images and shape matching Pattern recognition, including face detection Tracking and motion in 2 and 3 dimensions 3D reconstruction from stereo vision Machine learning algorithms Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.

Learning OpenCV Computer Vision with the OpenCV Library 1st Table of contents:

CHAPTER 1: Overview
What Is OpenCV?
Who Uses OpenCV?
What Is Computer Vision?
The Origin of OpenCV
Speeding Up OpenCV with IPP
Who Owns OpenCV?
Downloading and Installing OpenCV
Install
Windows
Linux
MacOS X
Getting the Latest OpenCV via CVS
More OpenCV Documentation
Documentation Available in HTML
Documentation via the Wiki
OpenCV Structure and Content
Portability
Exercises

CHAPTER 2: Introduction to OpenCV
Getting Started
First Program—Display a Picture
Second Program—AVI Video
Moving Around
A Simple Transformation
A Not-So-Simple Transformation
Input from a Camera
Writing to an AVI File
Onward
Exercises

CHAPTER 3: Getting to Know OpenCV
OpenCV Primitive Data Types
Matrix and Image Types
CvMat Matrix Structure
Accessing Data in Your Matrix
The easy way
The hard way
The right way
Arrays of Points
IplImage Data Structure
Accessing Image Data
More on ROI and widthStep
Matrix and Image Operators
Drawing Things
Lines
Circles and Ellipses
Polygons
Fonts and Text
Data Persistence
Integrated Performance Primitives
Verifying Installation
Summary
Exercises

CHAPTER 4: HighGUI
A Portable Graphics Toolkit
Creating a Window
Loading an Image
Displaying Images
WaitKey
Mouse Events
Sliders, Trackbars, and Switches
No Buttons
Working with Video
Reading Video
Writing Video
ConvertImage
Exercises

CHAPTER 5: Image Processing
Overview
Smoothing
Image Morphology
Dilation and Erosion
Making Your Own Kernel
More General Morphology
Opening and closing
Morphological gradient
Top Hat and Black Hat
Flood Fill
Resize
Image Pyramids
Threshold
Adaptive Threshold
Exercises

CHAPTER 6: Image Transforms
Overview
Convolution
Convolution Boundaries
Gradients and Sobel Derivatives
Scharr Filter
Laplace
Canny
Hough Transforms
Hough Line Transform
Hough Circle Transform
Remap
Stretch, Shrink, Warp, and Rotate
Affine Transform
Perspective Transform
CartToPolar and PolarToCart
LogPolar
Discrete Fourier Transform (DFT)
Integral Images
Distance Transform
Histogram Equalization
Exercises

CHAPTER 7: Histograms and Matching
Basic Histogram Data Structure
Accessing Histograms
Basic Manipulations with Histograms
Comparing Two Histograms
Histogram Usage Examples
Earth Mover’s Distance
Back Projection
Template Matching
Exercises

CHAPTER 8: Contours
Memory Storage
Sequences
Contour Finding
Drawing Contours
Polygon Approximations
Summary Characteristics
Matching Contours
Moments
Matching with Hu Moments
Hierarchical Matching
Contour Convexity and Convexity Defects
Exercises

CHAPTER 9: Image Parts and Segmentation
Parts and Segments
Background Subtraction
Scene Modeling
Frame Differencing
Advanced Background Method
Connected Components for Foreground Cleanup
Watershed Algorithm
Image Repair by Inpainting
Mean-Shift Segmentation
Delaunay Triangulation, Voronoi Tessellation
Exercises

CHAPTER 10: Tracking and Motion
The Basics of Tracking
Corner Finding
Subpixel Corners
Invariant Features
Optical Flow
Lucas-Kanade Method
Dense Tracking Techniques
Mean-Shift and Camshift Tracking
Motion Templates
The Kalman Filter
The Condensation Algorithm
Exercises

CHAPTER 11: Camera Models and Calibration
Camera Model
Lens Distortions
Calibration
Chessboards
Homography
Camera Calibration
Undistortion
Rodrigues Transform
Exercises

CHAPTER 12: Projection and 3D Vision
Projections
Affine and Perspective Transformations
POSIT: 3D Pose Estimation
Stereo Imaging
Triangulation
Epipolar Geometry
Stereo Calibration
Stereo Rectification
Stereo Correspondence
Depth Maps from 3D Reprojection
Structure from Motion
Exercises

CHAPTER 13: Machine Learning
What Is Machine Learning
Training and Test Set
OpenCV ML Algorithms
Common Routines in the ML Library
Naïve/Normal Bayes Classifier
Binary Decision Trees
Boosting
Random Trees
Face Detection or Haar Classifier
Other Machine Learning Algorithms
Exercises

CHAPTER 14: OpenCV’s Future
Past and Future
Directions
Specific Items
OpenCV for Artists
Afterword

People also search for Learning OpenCV Computer Vision with the OpenCV Library 1st:

learning opencv 2nd edition computer vision
    
learning opencv 5 computer vision with python
    
learning opencv pdf
    
learning computer vision
    
learning opencv 3 pdf

Tags: Adrian Kaehler, Gary Bradski, OpenCV Computer, OpenCV Library