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 …