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OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly for real-time computer vision. [2] Originally developed by Intel, it was later supported by Willow Garage, then Itseez (which was later acquired by Intel [3]). The library is cross-platform and licensed as free and open-source software under Apache License ...
A Block Matching Algorithm is a way of locating matching macroblocks in a sequence of digital video frames for the purposes of motion estimation.The underlying supposition behind motion estimation is that the patterns corresponding to objects and background in a frame of video sequence move within the frame to form corresponding objects on the subsequent frame.
In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties.
Gary Bradski is an American scientist, engineer, entrepreneur, and author. He co-founded Industrial Perception, a company that developed perception applications for industrial robotic application (since acquired by Google in 2012 [2]) and has worked on the OpenCV Computer Vision library, as well as published a book on that library.
Optical flow can be estimated in a number of ways. Broadly, optical flow estimation approaches can be divided into machine learning based models (sometimes called data-driven models), classical models (sometimes called knowledge-driven models) which do not use machine learning and hybrid models which use aspects of both learning based models and classical models.
OpenCV's Cascade Classifiers support LBPs as of version 2. VLFeat, an open source computer vision library in C (with bindings to multiple languages including MATLAB) has an implementation. LBPLibrary is a collection of eleven Local Binary Patterns (LBP) algorithms developed for background subtraction problem. The algorithms were implemented in ...
Connected-component labeling (CCL), connected-component analysis (CCA), blob extraction, region labeling, blob discovery, or region extraction is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic.
A rapidly exploring random tree (RRT) is an algorithm designed to efficiently search nonconvex, high-dimensional spaces by randomly building a space-filling tree.The tree is constructed incrementally from samples drawn randomly from the search space and is inherently biased to grow towards large unsearched areas of the problem.