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Geometry in computer vision is a sub-field within computer vision dealing with geometric relations between the 3D world and its projection into 2D image, typically by means of a pinhole camera. Common problems in this field relate to Reconstruction of geometric structures (for example, points or lines) in the 3D world based on measurements in ...
Geometric feature learning is a technique combining machine learning and computer vision to solve visual tasks. The main goal of this method is to find a set of representative features of geometric form to represent an object by collecting geometric features from images and learning them using efficient machine learning methods.
In computer vision and computer graphics, the 3D Morphable Model (3DMM) is a generative technique that uses methods of statistical shape analysis to model 3D objects. The model follows an analysis-by-synthesis approach over a dataset of 3D example shapes of a single class of objects (e.g., face, hand).
In computer vision, the fundamental matrix is a 3×3 matrix which relates corresponding points in stereo images.In epipolar geometry, with homogeneous image coordinates, x and x′, of corresponding points in a stereo image pair, Fx describes a line (an epipolar line) on which the corresponding point x′ on the other image must lie.
In the field of computer vision, any two images of the same planar surface in space are related by a homography (assuming a pinhole camera model). This has many practical applications, such as image rectification , image registration , or camera motion—rotation and translation—between two images.
Efficient PnP (EPnP) is a method developed by Lepetit, et al. in their 2008 International Journal of Computer Vision paper [9] that solves the general problem of PnP for n ≥ 4. This method is based on the notion that each of the n points (which are called reference points) can be expressed as a weighted sum of four virtual control points ...
The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999. [1] Applications include object recognition , robotic mapping and navigation, image stitching , 3D modeling , gesture recognition , video tracking , individual identification of ...
Formulas involving homogeneous coordinates are often simpler and more symmetric than their Cartesian counterparts. Homogeneous coordinates have a range of applications, including computer graphics and 3D computer vision, where they allow affine transformations and, in general, projective transformations to be easily represented by a matrix.