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PDF Introduction Marvin Smith to OpenCV

Introduction OpenCV is an Image Processing library created by Intel and maintained by Willow Garage Available for C C++ and Python Newest update is version 2 2 Open Source and free Easy to use and install

PDF OpenCV

Introduction to the Eigen library Optimizing your algorithms using ARM NEON

  • What is practical OpenCV?

    Practical OpenCV is a hands-on project book that shows you how to get the best results from OpenCV, the open-source computer vision library. Author uses OpenCV and its Java bindings JavaCV for examples. An introductory computer vision book that takes an example driven, hands on approach.

  • Who started this OpenCV tutorial?

    This tutorial has been started by Abid Rahman K. as part of Google Summer of Code 2013 program, under the guidance of Alexander Mordvintsev. OpenCV Needs You !!! Since OpenCV is an open source initiative, all are welcome to make contributions to this library. And it is same for this tutorial also.

  • How do I download OpenCV?

    You can download the latest release of OpenCV from sourceforge site. Then extract the folder. Or you can download latest source from OpenCV’s github repo. (If you want to contribute to OpenCV, choose this. It always keeps your OpenCV up-to-date). For that, you need to install Git first.

  • Does OpenCV use CV namespace?

    OpenCV uses the cv namespace. cv::Mat object replaces the original C standard IplImage and CvMat classes. All original functions and classes of the C standard OpenCV components in the Bradski book are still available and current. However you will need to read that book for it. namedWindow is used for viewing images.

Roadmap

Introduction to the Eigen library Optimizing your algorithms using ARM NEON web.stanford.edu

OpenCV

An open source BSD licensed computer vision library Patent-encumbered code isolated into “non-free” module. (This includes SIFT) Available on all major platforms Android, iOS, Linux, Mac OS X, Windows Written primarily in C++ Bindings available for Python, Java,

InputArray / OutputArray

InputArray and OutputArray are proxy classes. Based on the function, they can be concretely substituted by cv::Mat and/or std::vector. An InputArray is immutable (const-enforced). // Converts point coordinates from normal pixel coordinates // to homogeneous coordinates ((x,y)->(x,y,1)) void convertPointsToHomogeneous(InputArray src, OutputArray dst

Stack-based matrices

Mat allocates memory on the heap. We want to avoid allocating memory on the heap. Dynamic memory allocation is expensive, can interfere with cache locality. The Matx type is suitable for small matrices. Allocated on the stack. Matx can usually be used wherever you’d use a Mat (a few exceptions exist). // Use OpenCV's implementation of the Rodrig

Live Demo

Let’s build an optical flow tracker. (We’ll have a whole lecture on optical flow later) We’ll code on Mac OS X, deploy to iOS. The core vision code remains the same across platforms. Trivial to port to Android. web.stanford.edu

// Using SVD:

xx = A.jacobiSvd(ComputeThinU ComputeThinV).solve(b); cout << "x = " << x << "\ "; web.stanford.edu

// Using QR decomposition:

xx = A.colPivHouseholderQr().solve(b); cout << "x = " << x << "\ "; web.stanford.edu

// Using the normal equations:

xx = (A.transpose()*A).ldlt().solve(A.transpose()*b); cout << "x = " << x << "\ "; web.stanford.edu

Eigen Quick Reference

Elegant API for matrix operations. Includes both fixed and dynamic matrices. Static consistency checks A more complete reference is available. web.stanford.edu

Aligned allocations

Structs containing Eigen’s fixed size vectorizable objects need to ensure that they’re aligned. Required for SIMD operations (SSE). Not necessary for dynamically allocated objects. More details available over here and here. class Landmark { private: Vector3f position; Quaternionf orientation; public: web.stanford.edu

Other Libraries

The Point Cloud Library (PCL) Framework for working with 3D point clouds. CCV Implements many modern vision algorithms (Predator, DPMs,

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