L7(Edges)

L7(Edges) - Edge Detection CS / ECE 181B Thursday, April...

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Edge Detection CS / ECE 181B Thursday, April 22, 2004 ± Edge detection ± Linear filtering ± Laplacian of the Gaussian filter Edge Detection • Edge detection is a local area operator that seeks to find significant, meaningful changes in image intensity (color?) that correspond to – Boundaries of objects and patterns – Texture – Changes in object color or brightness – Highlights – Occlusions – Etc.
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Useful Mathematics Funcs. Rx x x xx () = < = > ± ² ³ ´ ³ 00 0 Ux x x x = < = > ± ² ³ ´ ³ 1 2 0 10 ± x x x x = < = > ² ³ ´ µ ´ Ramp Step Impulse dx ± d dx dx ± d dx The bad news • Unfortunately, it’s very hard to tell significant edges from bogus edges! – Noise is a big problem! • An edge detector is basically a high-frequency filter, since sharp intensity changes are high-frequency events • But image noise is also high-frequency, so edge detectors tend to accentuate noise! • Some things to do: – Smooth before edge detection (hoping to get rid of noise but not edges!) – Look for edges at multiple scales (pyramids!) – Use an adaptive edge threshold
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Caveats • In reality, low light levels and random noise lead to high fluctuations in individual pixel values, leading to bad estimations. Edge detection history Edge detection has a long history and a huge literature – Edge modeling: Step edges, roof edges, impulse edges… – Biological modeling: How does human vision do it? – Elegant and complex mathematical models – Simple and computationally cheap edge detectors – Etc., etc., etc…. .
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This note was uploaded on 12/29/2011 for the course ECE 181b taught by Professor Staff during the Fall '08 term at UCSB.

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L7(Edges) - Edge Detection CS / ECE 181B Thursday, April...

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