Local features and their descriptors are the building blocks of many computer vision algorithms. Their applications include image registration, object detection and classification, tracking, motion estimation, and content-based image retrieval (CBIR). These algorithms use local features to better handle scale changes, rotation, and occlusion. Computer Vision Toolbox™ algorithms include the FAST, Harris, and Shi & Tomasi corner detectors, and the SURF, KAZE, and MSER blob detectors. The toolbox includes the SURF, FREAK, BRISK, LBP, ORB, and HOG descriptors. You can mix and match the detectors and the descriptors depending on the requirements of your application.
Local Feature Detection and Extraction
Learn the benefits and applications of local feature detection and extraction
Choose functions that return and accept points objects for several types of features
Specify pixel Indices, spatial coordinates, and 3-D coordinate systems
When you specify the type of shape to draw, you must also specify it’s location on the image.
Image Retrieval with Bag of Visual Words
Retrieve images from a collection of images similar to a query image using a content-based image retrieval (CBIR) system.