I think developers and research guys who works with object recognition, image registration and other areas that uses keypoint extraction can find this post useful. Recently (from 2.4.2) a new feature descriptor algorithm was added to OpenCV library. FREAK descriptor is claimed to be superior to ORB and SURF descriptors, yet it’s very fast (comparable to ORB). Also people in comments on my blog mentioned BRISK descriptor which is also new and more efficient than SURF.
[toc] Hi folks! I’m glad to publish a sixth part of the OpenCV Tutorial cycle. In this post I will describe how to implement interesting non-photorealistic effect that makes image looks like a cartoon. It has numerous names: cartoon filter or simply “toon” also it known as rotoscoping. In addition we will refactor application interface and add tweeting feature to share your results across the web. According to the roadmap I promised to put the video recording module too, but due to lack of free time I decided to put it on hold for now.
Hello readers! The fifth part of the OpenCV Tutorial is here! In this post we will add options pane for our samples. In the end of this chapter our application will receive options interface as shown on screenshot. But first, let me remind you (if you came here for the first time) what is happening here. The “OpenCV Tutorial” is a open-source project maintained by me (Eugene Khvedchenya). My goal - create a iPhone/iPad application to demonstrate various image processing algorithms of OpenCV library and how to use them in iOS applications.
This is the fourth part of the OpenCV Tutorial. In this part the solution of the annoying iOS video capture orientation bug will be described. Of course that’s not all. There are some new features - we will add processing of saved photos from your photo album. Also to introduce minor interface improvements and I’ll show you how to disable unsupported API like video capture in your app and run in on iOS Simulator.
In Part 1 and Part 2 we created base application for our “OpenCV Tutorial” application. In this part we add video source to process frames using our samples and present the result to user. As usual, you can find source code for this application at github. Video capture in iOS At this moment (as far as i know) there OpenCV’s cv::VideoCapture does not support iOS platform. Therefore we have to use iOS AVFoundation API to setup video capture.
In the previous step we created Master-Detail XCode project and linked OpenCV library to it. Also we defined a base interface for all samples. Today we’ll write some UI logic to integrate our samples into the application. One ring to rule them all Since we are going to store a lot of samples (i hope so), we have to store them somewhere. I think for our application the ideal place to save them is our application delegate class.
As i recently mentioned, i decided to write a brand new OpenCV tutorial application for iPhone/iPad devies. This development is open-source and anyone can access it on https://github.com/BloodAxe/OpenCV-Tutorial repository page. Your help are welcome to write a UI for this app and help writing sample demonstration cases. Feel free to clone repository and make your contribution! OpenCV Tutorial The application startup screen will present master-detail view as a list of available samples.
A lot of changes been made since I posted a tutorial of using OpenCV library on iPhone/iPad devices. It was the time of iOS 4.x and OpenCV 2.1. The time to rewrite the whole sample project has come. With this post I announce that I going to update my sample project and I ask for your help. My idea is to write a project which will demonstrate use of several common computer vision algorithms.
My first post in this blog was about building OpenCV for iOS devices (iPhone, iPad, iPod and so on). But the build process that i used is not trivial at all. I received a lot of feedbacks and questions about building OpenCV, setting up XCode build environment. Today i made your life much easier. I have a gift - a build script, which will **build OpenCV **library for your iPhone, iPad, iPod or any other iOS based Apple device right in one click!
A brief tutorial/intro to the mathematical morphology in image processing. Basic Definitions The term morphology refers to the description of the properties of shape and structure of any objects. In the context of computer vision, this term refers to the description of the properties of shapes of areas on the image. Operations of mathematical morphology were originally defined as operations on sets, but it soon became clear that they are also useful in the processing tasks of the set of points in the two-dimensional space.