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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
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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….. 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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