ArduinoComputerVisionProgramming - (EPUB全文下载)

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书籍内容：

Arduino Computer Vision Programming
Table of Contents
Arduino Computer Vision Programming
Credits
About the Authors
About the Reviewers
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Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. General Overview of Computer Vision Systems
Introducing computer vision systems
Approaching computer vision problems
Data acquisition
Preprocessing
Feature extraction by image processing
Post-processing and post-filtering
Recognition or detection
Acting in the real world
Connecting the pieces
Summary
2. Fundamentals and Installation of OpenCV
Fundamentals of OpenCV
The installation of OpenCV
Installing OpenCV on Linux
Installing and configuring packages
Using OpenCV with Eclipse CDT
Installing Eclipse CDT
Installing OpenCV on Mac OS
Getting command-line tools
Installing HomeBrew
Using OpenCV in Xcode
Installing OpenCV on Windows
Installing MS Visual Studio 2013
OpenCV on iOS
OpenCV on Android
Installing OpenCV4Android
Eclipse integration
Running samples
Summary
3. Data Acquisition with OpenCV and Arduino
Image and video acquisition
Camera selection
Resolution
Color capabilities
Frame rate
2D or 3D
Communication interface
Image acquisition
Reading a static image
Taking a snapshot from a webcam
Getting a video stream from the webcam
Interaction with Kinect
Integration of Kinect with OpenCV
Installing on a Mac
Xcode integration
Sensor data acquisition
Setting up an Arduino environment
Fundamentals of sensors
Sensor types
Sampling theorem
Dealing with noise
Reading data from the temperature sensor
Summary
4. Filtering Data with OpenCV
Getting started with filtering
2D convolution
Spatial domain filtering
Smoothing
Sharpening
Color conversions
Grayscale
Binary
Constant thresholding
Adaptive thresholding
Morphological filters
Erosion and dilation
Erosion
Dilation
Opening and closing
Closing
Opening
Gradients and edge detection
The Canny edge detector
Laplacian of Gaussian filter
Sobel
Custom filtering
Histogram equalization
Chapter project
Summary
5. Processing Vision Data with OpenCV
Extracting features
Using basic statistics
Using color features
Using template matching features
Using contours
Using the convex hull
Using moments
Using the Hough transform
Using corners
Using SIFT
Using SURF
Usi ............

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