31. October 2014
01. October 2014
Third computer vision digest. Your monthly portion of news in computer vision for September 2014.
In this issue:
- Real-time face 3D model reconstruction
- Image color correction and contrast enhancement
- Robust Optimization Techniques in Computer Vision
- Computer Vision Digest (May 2014)
- Computer Vision Digest (June 2014)
- Computer Vision Digest (August 2014)
11. September 2014
During development of CloudCV I came to the problem on converting
native C++ data types in my Node.js native module. If you are new to C++ and Node.js, I suggest you to read how to write C++ modules for Node.js and connecting OpenCV and Node.js first.
Mapping V8 data types to native C++ equivalents is trivial, but somewhat wordy. One should take the argument at given index, check whether it is defined, then check it’s type and finally cast to C++ type. This works fine while you have function that receive two or three arguments of trivial type (That can be mapped directly to built-in C++ types). What about strings? Arrays? Complex types like objects or function callback? You code will grow like and became hard-to-maintain pasta-code some day.
In this post I present my approach on solving this problem with a laconic way on describing what do you expect as input arguments.
30. August 2014
Third computer vision digest. Your monthly portion of news in computer vision for August 2014.
In this issue:
- Free Photo Editing Software Lets You Manipulate Objects in 3D
- Real-Time Digital Makeup with Projection Mapping
- Video stabilization through 3D scene recovery
- Using OpenCV, Python and Template Matching to play “Where’s Waldo?”
- OpenCV 3.0 alpha is out
16. August 2014
Eigen is a C++ template library for matrix and vector operations. It is highly optimized for numeric operations and support vectorization and use aligned memory allocators.
When it comes to matrix operations, Eigen is much faster than OpenCV. However, it can be situations when it is necessary to pass Eigen data to OpenCV functions.
In this post I will show how to map Eigen data to OpenCV with easy and efficient way. No copy, minimal overhead and maximum syntax sugar:
Eigen::ArrayXXd img(480, 640); ... cv::imshow("test", eigen2cv(img));
Proposed approach does not limited to continuous memory layout - it support expression and blocks as well. If given expression has to be evaluated - it will be evaluated into temporary dense storage and then mapped to OpenCV structure:
// Unsharp mask Eigen::ArrayXXd img, blur; cv::GaussianBlur(eigen2cv(img), eigen2cv(blur)); cv::imshow("sharpened", eigen2cv(1.5 * img - 0.5 * blur));
07. August 2014
Let me introduce you Adrian Rosebrock and his http://www.pyimagesearch.com/ website. It’s about computer vision and image processing using Python and OpenCV. Looks like there are more than one person that like to share programming experience via blogging :)
Here’s how Adrian position himself:
This blog is dedicated to helping other programmers understand how image search engines work. While a lot of computer vision concepts are theoretical in nature, I’m a big fan of “learning by example”. My goal is to distill my life experiences in building image search engines into concise, easy to understand examples.