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Features from accelerated segment test (FAST) is a corner detection method, which could be used to extract feature points and later used to track and map objects in many computer vision tasks. The FAST corner detector was originally developed by Edward Rosten and Tom Drummond, and was published in 2006. [1] The most promising advantage of the ...
A track algorithm is a radar and sonar performance enhancement strategy. Tracking algorithms provide the ability to predict future position of multiple moving objects based on the history of the individual positions being reported by sensor systems. Historical information is accumulated and used to predict future position for use with air ...
In computer vision, the Lucas–Kanade method is a widely used differential method for optical flow estimation developed by Bruce D. Lucas and Takeo Kanade.It assumes that the flow is essentially constant in a local neighbourhood of the pixel under consideration, and solves the basic optical flow equations for all the pixels in that neighbourhood, by the least squares criterion.
The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. [1] [2] It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes. The algorithm is efficient for its time, able to detect faces in ...
Detection and tracking of point features. In the second paper Tomasi and Kanade used the same basic method for finding the registration due to the translation but improved the technique by tracking features that are suitable for the tracking algorithm. The proposed features would be selected if both the eigenvalues of the gradient matrix were ...
Harris corner detector. The Harris corner detector is a corner detection operator that is commonly used in computer vision algorithms to extract corners and infer features of an image. It was first introduced by Chris Harris and Mike Stephens in 1988 upon the improvement of Moravec's corner detector. [1] Compared to its predecessor, Harris ...
v. t. e. The Louvain method for community detection is a method to extract non-overlapping communities from large networks created by Blondel et al. [1] from the University of Louvain (the source of this method's name). The method is a greedy optimization method that appears to run in time where is the number of nodes in the network.
The MSER algorithm can be used to track colour objects, by performing MSER detection on the Mahalanobis distance to a colour distribution. By detecting MSERs in multiple resolutions, robustness to blur, and scale change can be improved. Other applications. Shape Descriptors for Maximally Stable Extremal Regions