Computer Vision in ArrayFire – Part 2: Feature Description and Matching

Peter Entschev ArrayFire, Benchmarks, Computer Vision 2 Comments

In the Part 1 of this series, we talked about upcoming feature detection algorithms in ArrayFire library. In this post we show case some of the preliminary results of Feature Description and matching that are under development in the ArrayFire library. Feature description is done using the ORB feature descriptor[1]. The descriptors are matched against a database of features using Hamming distance as the metric. The results we show in this blog use the same hardware and software used in the previous blog: Intel Sandy Bridge Xeon processor with 32 cores (for baseline OpenCV CPU implementation) NVIDIA Tesla K20C (for OpenCV and ArrayFire CUDA implementations) ArrayFire development version OpenCV version 2.4.9 Feature Description and Matching Benchmarks In Part 1 we showed that …

Computer Vision in ArrayFire – Part 1: Feature Detection

Peter Entschev ArrayFire, Computer Vision 6 Comments

A few weeks ago we wrote Writing a Simple Corner Detector with ArrayFire. In that post, we discussed a little bit about the new features that we are working on for ArrayFire. Some of these new computer vision features will be available in the next release of ArrayFire. For the next release, ArrayFire will have a complete set to start with feature tracking, including FAST for feature detection [1], ORB for description [2] and a Hamming distance matcher. We will also include a dedicated version of the Harris corner detector [3], even though it can be written using existing ArrayFire functions. This implementation is straightforward, easy to use and will have better performance. For this post, we will share some …

Image Processing with ArrayFire and OpenCV on the GPU

John Melonakos ArrayFire, C/C++, Case Studies, CUDA Leave a Comment

ArrayFire is a great way to supplement OpenCV for faster processing on the GPU. Mcclanahoochie recently posted an interactive demo showing the use of OpenCV with ArrayFire for computing Local Contrast Enhancement on the GPU from webcam video. Mcclanahoochie also shows how easy it is to convert OpenCV Mat images into ArrayFire GPU array images, as seen in the code snippit below: All the source code is available on Google Code, linked to from his website. Simply download ArrayFire and OpenCV and try it out for yourself!

Fast Computer Vision with OpenCV and ArrayFire

John Melonakos ArrayFire, Benchmarks, Case Studies, CUDA Leave a Comment

Update:  While the post below discusses LibJacket (no longer a product), you can do the same thing in the newer, but different, ArrayFire library.  Improved performance benchmarks and a simpler API are the results of moving from LibJacket to ArrayFire. Mcclanahoochie just posted some code and instructions for pairing OpenCV with LibJacket to get accelerated computer vision.  You can do really fast image processing on video cam feeds too, see picture below: Really cool stuff.  Computer vision is really hot with applications emerging in defense, radiology, games, automotive, and other consumer applications. Computer vision algorithms like these are also going mobile.  For instance, we have started to build LibJacket for Mobile applications, which runs on Tegra, PowerVR, and other mobile …