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Sheet Harris Corner Detection

This document describes the Harris corner detection algorithm. It involves computing derivatives Ix and Iy in a local window to form a Harris matrix. The Harris cornerness score R is then computed from the Harris matrix as the determinant minus a constant times the trace squared. A high positive R value indicates a corner, a value near zero indicates an edge, and a low negative value indicates a flat area.

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Begad Habib
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250 views1 page

Sheet Harris Corner Detection

This document describes the Harris corner detection algorithm. It involves computing derivatives Ix and Iy in a local window to form a Harris matrix. The Harris cornerness score R is then computed from the Harris matrix as the determinant minus a constant times the trace squared. A high positive R value indicates a corner, a value near zero indicates an edge, and a low negative value indicates a flat area.

Uploaded by

Begad Habib
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Sheet Harris Corner Detection

Consider the following image:

Compute the Harris matrix

for the 3 by 3 highlighted window. In the above formula Ix = dI/dx, Iy = dI/dy, and
W is the window highlighted in the image.

A) First, compute the derivatives using the differentiation kernels shown


above.
B) Now compute the Harris Matrix based on the derivative matrices.
C) Compute the Harris cornerness score R = det(H) − k trace(H)2 for k = 0.04.
What do we have here? A corner? An edge? Or a flat area? Why?

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