ECE
ECE468_7

# ECE468_7 - ECE 468 CS 519 Digital Image Processing...

• Notes
• 42

This preview shows pages 1–12. Sign up to view the full content.

Prof. Sinisa Todorovic [email protected] ECE 468 / CS 519: Digital Image Processing Gradients, Harris Corners 1

This preview has intentionally blurred sections. Sign up to view the full version.

Image Gradient along X-axis I x ( x, y ) = I ( x + 1 , y ) - I ( x, y ) = 2 4 0 0 0 0 - 1 1 0 0 0 3 5 | {z } D x ( x,y ) I ( x, y ) 2
Image Gradient along Y-axis = 2 4 0 0 0 0 - 1 0 0 1 0 3 5 | {z } D y ( x,y ) I ( x, y ) I y ( x, y ) = I ( x, y + 1) - I ( x, y ) 3

This preview has intentionally blurred sections. Sign up to view the full version.

Filtering Image Gradient convolution is associative w ( x, y ; σ ) I x ( x, y ) = w ( x, y ; σ ) D x ( x, y ) I ( x, y ) = [ w ( x, y ; σ ) D x ( x, y )] I ( x, y ) 4
= [ D x ( x, y ) w ( x, y ; σ )] I ( x, y ) Filtering Image Gradient w ( x, y ; σ ) I x ( x, y ) = w ( x, y ; σ ) D x ( x, y ) I ( x, y ) = [ w ( x, y ; σ ) D x ( x, y )] I ( x, y ) convolution is commutative 5

This preview has intentionally blurred sections. Sign up to view the full version.

= [ D x ( x, y ) w ( x, y ; σ )] I ( x, y ) Filtering Image Gradient w ( x, y ; σ ) I x ( x, y ) = w ( x, y ; σ ) D x ( x, y ) I ( x, y ) = [ w ( x, y ; σ ) D x ( x, y )] I ( x, y ) = w x ( x, y ; σ ) I ( x, y ) derivative of the filter 6
Weighted Image Gradient Image is discrete Gradient is approximate We always find the gradient of the filter ! w ( x, y ; σ ) I x ( x, y ) = w x ( x, y ; σ ) I ( x, y ) w ( x, y ; σ ) I y ( x, y ) = w y ( x, y ; σ ) I ( x, y ) 7

This preview has intentionally blurred sections. Sign up to view the full version.

Interest Points Harris corners 8
Properties of Interest Points Locality -- robust to occlusion, noise Saliency -- rich visual cue Stable under affine transforms Distinctiveness -- differ across distinct objects Efficiency -- easy to compute 9

This preview has intentionally blurred sections. Sign up to view the full version.

Example of Detecting Harris Corners 10
Harris Corner Detector homogeneous region no change in all directions edge no change along the edge corner change in all directions Source: Frolova, Simakov, Weizmann Institute scanning window 11

This preview has intentionally blurred sections. Sign up to view the full version.

This is the end of the preview. Sign up to access the rest of the document.
• Fall '08
• Lucchese
• Image processing, Computer vision, Eigenvalue, eigenvector and eigenspace, Harris corners, Harris corner detector, automatic scale selection

{[ snackBarMessage ]}

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern