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Learning OpenCV
Computer Vision with the OpenCV Library
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- Contents
-
+
Preface
- + CHAPTER 1: Overview
- + CHAPTER 2: Introduction to OpenCV
-
+
CHAPTER 3: Getting to Know OpenCV
- + OpenCV Primitive Data Types
- + CvMat Matrix Structure
- + IplImage Data Structure
-
+
Matrix and Image Operators
- cvAbs, cvAbsDiff, and cvAbsDiffS
- cvAdd, cvAddS, cvAddWeighted, and alpha blending
- cvAnd and cvAndS
- cvAvg
- cvAvgSdv
- cvCalcCovarMatrix
- cvCmp and cvCmpS
- cvConvertScale
- cvConvertScaleAbs
- cvCopy
- cvCountNonZero
- cvCrossProduct
- cvCvtColor
- cvDet
- cvDiv
- cvDotProduct
- cvEigenVV
- cvFlip
- cvGEMM
- cvGetCol and cvGetCols
- cvGetDiag
- cvGetDims and cvGetDimSize
- cvGetRow and cvGetRows
- cvGetSize
- cvGetSubRect
- cvInRange and cvInRangeS
- cvInvert
- cvMahalonobis
- cvMax and cvMaxS
- cvMerge
- cvMin and cvMinS
- cvMinMaxLoc
- cvMul
- cvNot
- cvNorm
- cvNormalize
- cvOr and cvOrS
- cvReduce
- cvRepeat
- cvScale
- cvSet and cvSetZero
- cvSetIdentity
- cvSolve
- cvSplit
- cvSub
- cvSub, cvSubS, and cvSubRS
- cvSum
- cvSVD
- cvSVBkSb
- cvTrace
- cvTranspose and cvT
- cvXor and cvXorS
- cvZero
- + Drawing Things
- Data Persistence
- + Integrated Performance Primitives
- Summary
- Exercises
-
+
CHAPTER 4: HighGUI
-
+
CHAPTER 5: Image Processing
-
+
CHAPTER 6: Image Transforms
- Overview
- + Convolution
- + Gradients and Sobel Derivatives
- Laplace
- Canny
- + Hough Transforms
- Remap
-
+
Stretch, Shrink, Warp, and Rotate
- CartToPolar and PolarToCart
- LogPolar
- + Discrete Fourier Transform (DFT)
- Discrete Cosine Transform (DCT)
- Integral Images
- Distance Transform
- Histogram Equalization
- Exercises
-
+
CHAPTER 7: Histograms and Matching
-
+
CHAPTER 8: Contours
- + CHAPTER 9: Image Parts and Segmentation
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+
CHAPTER 10: Tracking and Motion
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+
CHAPTER 11: Camera Models and Calibration
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+
CHAPTER 12: Projection and 3D Vision
- + CHAPTER 13: Machine Learning
- + CHAPTER 14: OpenCV’s Future
- Bibliography
- Index
- About the Authors
- Colophon
"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.
Test the closed alpha on paperc.com
Book Details
Authors
Gary Bradski and Adrian Kaehler
Categories
Computers > Computer Vision & Pattern Recognition
Publishers
Publication year : 2008
License: All rights reserved ©
Times read: 7,750
