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Hough Transform

Dr. Shree Nayar of Columbia University gave a presentation on the Hough transform, a technique for line detection in images. The Hough transform works by having each edge point in an image vote for possible line parameters in a parameter space, with the line parameters corresponding to the highest number of votes indicating the most prominent lines in the original image. She provided examples of applying the Hough transform to an image after edge detection to detect the 20 most prominent lines.

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105 views71 pages

Hough Transform

Dr. Shree Nayar of Columbia University gave a presentation on the Hough transform, a technique for line detection in images. The Hough transform works by having each edge point in an image vote for possible line parameters in a parameter space, with the line parameters corresponding to the highest number of votes indicating the most prominent lines in the original image. She provided examples of applying the Hough transform to an image after edge detection to detect the 20 most prominent lines.

Uploaded by

Lovely doll
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Dr.

Shree Nayar, Columbia University


Background Knowledge
Line Fitting Real World
Solution is HOUGH transform
A line in the image corresponds to a point in Parameter (Hough) Space.
A line in the image corresponds to a point in Parameter (Hough) Space.
• Let each edge point in image space vote for a set of possible
parameters in Hough space
• Let each edge point in image space vote for a set of possible
parameters in Hough space
• Accumulate votes in discrete set of bins; parameters with the
most votes indicate line in image space.
(m, c) = (1, 0)
Point in image space is now sinusoid segment in Hough space.
If p can only be positive, then will have to go for 0 to 2Pi. 0 to .
If p can be positive or negative, then only has to go from 0 to Pi or 0 to –Pi. Is .
Natural scene and result of Sobel edge detection followed by thresholding.
Accumulator matrix
Original image and 20 most prominent lines.

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