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L7(Edges)

# L7(Edges) - Edge Detection CS ECE 181B Thursday Edge...

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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. R x x x x x ( ) = < = > ± ² ³ ´ ³ 0 0 0 0 0 U x x x x ( ) = < = > ± ² ³ ´ ³ 0 0 1 2 0 1 0 ± ( ) x x x x = < = > ² ³ ´ µ ´ 0 0 1 0 0 0 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
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….. Typical usage: Detect “edge points” in the image (filter then threshold) ± Edges may have magnitude and orientation Throw away “bad” ones (isolated points)

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