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I am trying to understand the concept of homography. It gives features but I can't get that how does it calculate features from images?

mrtpk
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Muhammad Ahmed
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  • The homography matrix is a 3X3 matrix which when multiplied to a pixel location gives a new location for that pixel. These links will help you understand this- https://www.youtube.com/watch?v=kYB8IZa5AuE&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab&index=3 http://people.scs.carleton.ca/~c_shu/Courses/comp4900d/notes/homography.pdf https://www.learnopencv.com/homography-examples-using-opencv-python-c/ – mrtpk Dec 10 '19 at 08:54
  • https://stackoverflow.com/questions/42581296/homography-and-affine-transformation https://courses.cs.washington.edu/courses/csep576/11sp/pdf/Transformations.pdf – mrtpk Dec 10 '19 at 09:24

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A homography is nothing but a mapping between points on one surface or plane to points on another. In the case of computer vision, it is a matrix specifying the transformation between two views of images, for example.

Two views between which a homography can be calculated

A homography can be estimated by identifying the keypoints in both the images and then estimating the transformation between the two views. There are many keypoint descriptors available that help in identifying these keypoints.

In camera calibration, an extrinsic matrix is computed by capturing different views of an object of known geometry, like a Chessboard from which the corner points are detected. The matrix is estimated by mathematically solving for the detected points from the many different views captured.

A details derivation of the estimation and solving to obtain the homography matrix can be found in this book. :)

Lalith Nag
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