A few weeks ago we added some computer vision functionality to our open source ArrayFire GPU computing library. Specifically, we implemented the FAST feature extractor, BRIEF feature point descriptor, ORB multi-resolution scale invariant feature extractor, and a Hamming distance function. When combined, these functions enable you to find features in videos (or images) and track them between successive frames.
GTC 2015 ArrayFire Recordings
Missed visiting ArrayFire at GTC this year? We’ve got you covered! You can now check out the recordings of all our GTC 2015 talks and tutorials at your own convenience. Learn about accelerating your code from the best in the business. Talks Real-Time and High Resolution Feature Tracking and Object Recognition Peter Andreas Entschev This session will cover real-time feature tracking and object recognition in high resolution videos using GPUs and productive software libraries including ArrayFire. Feature tracking and object recognition are computer vision problems that have challenged researchers for decades. Over the last 15 years, numerous approaches were proposed to solve these problems, some of the most important being SIFT, SURF and ORB. Traditionally, these approaches are so computationally …
Conway’s Game of Life using ArrayFire
Conway’s Game of Life is a popular zero player cellular automaton devised by the John Horton Conway in 1970. The game makes for a fun evolution as the player sets the initial condition and then observes the evolution of the game. Each cell has 2 states: live or dead. There are 4 simple rules that determine this: Any live cell with fewer than two live neighbours dies, as if caused by under-population. Any live cell with two or three live neighbours lives on to the next generation. Any live cell with more than three live neighbours dies, as if by overcrowding. Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction. From a programmer’s …
Image editing using ArrayFire: Part 3
Today, we will be doing the third post in our series Image editing using ArrayFire. References to old posts are available below. * Part 1 * Part 2 In this post, we will be looking at the following operations. Image Histogram Simple Binary Theshold Otsu Threshold Iterative Threshold Adaptive Binary Threshold Emboss Filter Today’s post will be mostly dominated by different types of threshold operations we can achieve using ArrayFire. Image Histogram We have a built-in function in ArrayFire that creates a histogram. The input image was converted to gray scale before histogram calculation as our histogram implementation works for vector and 2D matrices only. In case, you need histogram for all three channels of a color image, you can …
Image editing using ArrayFire: Part 2
A couple of weeks back, we did a post on a few image editing functions using ArrayFire library. Today, we shall be doing the second post in the series Image Editing using ArrayFire. We will be looking at the following operations today. Image distortion Noise addition Noise reduction Edge filters Boundary extraction Difference of gaussians Code and sample input/outputs corresponding to each operation are described below. Image distortion We will be looking at spread and pick filters in this section. Both of these filters are fundamentally the same, they replace each pixel in the original image with one of it’s neighboring pixels. How the neighbor is chosen is essentially the difference between spread and pick. Both of these functions use …
Computer Vision in ArrayFire – Part 2: Feature Description and Matching
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 …
Image editing using ArrayFire
In this post, we will be looking at the following simple image editing operations using the ArrayFire library. contrast modification brightness modification translation digital zoom alpha blending unsharp mask Code required to do each operation and the corresponding input/output sample are given below in their corresponding sections. All the operations are built using some existing image manipulation functions and the awesome element-wise operations in ArrayFire. Contrast modification /** * contrast value should be in the range [-1,1] **/ void changeContrast(array &in, const float contrast) { float scale = tan((contrast+1)*Pi/4); in = ((in/255.0f – 0.5f) * scale + 0.5f) * 255.0f; } Brightness modification /** * brightness value should be in the range [0,1] **/ void changeBrightness(array &in, const float brightness, …
Computer Vision in ArrayFire – Part 1: Feature Detection
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 …
Writing a Simple Corner Detector with ArrayFire
In the upcoming months we’ll be adding a lot of new Computer Vision functionality to ArrayFire, specifically targeting the most commonly used applications in this field. New functions include feature tracking, object classification, scene segmentation, optical flow, and stereo-vision. Feature tracking consists of three basic steps: Detecting good or unique features; normally they are corners or blobs of an object. Extracting a descriptor for each feature—understanding the texture of a small patch around each feature. Descriptor matching—finding out the best match for each pair of descriptors (one from the object being tracked, another from a scene that potentially contains that object), if any. Harris corner detector In this article we will be using ArrayFire to dive deeper into the first step of feature …
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