Search results
Results From The WOW.Com Content Network
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]).
A C++ implementation is available on GitHub, which has also been ported to OpenCV and included in the Camera Calibration and 3D Reconstruction module (SolvePnP function). [ 12 ] Using RANSAC
Active contour model, also called snakes, is a framework in computer vision introduced by Michael Kass, Andrew Witkin, and Demetri Terzopoulos [1] for delineating an object outline from a possibly noisy 2D image.
mlpack contains a C++ implementation of k-means. Octave contains k-means. OpenCV contains a k-means implementation. Orange includes a component for k-means clustering with automatic selection of k and cluster silhouette scoring. PSPP contains k-means, The QUICK CLUSTER command performs k-means clustering on the dataset. R contains three k-means ...
cvsba Archived 2013-10-24 at the Wayback Machine: An OpenCV wrapper for sba library . GPL. ssba: Simple Sparse Bundle Adjustment package based on the Levenberg–Marquardt Algorithm (C++). LGPL. OpenCV: Computer Vision library in the Images stitching module. BSD license. mcba: Multi-Core Bundle Adjustment (CPU/GPU). GPL3.
Algorithms that construct convex hulls of various objects have a broad range of applications in mathematics and computer science.. In computational geometry, numerous algorithms are proposed for computing the convex hull of a finite set of points, with various computational complexities.
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.
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]