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Image Filtering - Introduction to Computer Version - Lecture Sli, Lecture notes of Computer Science
These are the Lecture Slides of Introduction to Computer Version which includes Machine Learning, Framework, Prediction Function, Feature Representation, Image, Desired Output, Prediction Function, Prediction Error, Predicted Value etc. Key important points are: Image Filtering, Filtering, Spatial Domain, Frequency Domain, Mathematical Operation, Grid of Numbers, Smoothing, Sharpening, Measuring Texture, Image Compression
Typology: Lecture notes
2012/2013
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Motion illusion, rotating snakes
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Image Filtering Computer Vision
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Image filtering
- Image filtering: compute function of local neighborhood at each position
- Really important!
- Enhance images
- Denoise, resize, increase contrast, etc.
- Extract information from images
- Texture, edges, distinctive points, etc.
- Detect patterns
- Template matching
- Enhance images
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1 1 1 1 1 1 1 1 1 Slide credit: David Lowe (UBC) g[ ,] Example: box filter
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f [.,.] h [.,.] Image filtering
g[ ,]
Credit: S. Seitz
[ , ] [ , ] [ , ] , h m n g k l f m k n l k l
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f [.,.] h [.,.] Image filtering
g[ ,]
Credit: S. Seitz
[ , ] [ , ] [ , ] , h m n g k l f m k n l k l
Docsity.com
f [.,.] h [.,.] Image filtering
g[ ,]
Credit: S. Seitz
[ , ] [ , ] [ , ] , h m n g k l f m k n l k l
Docsity.com
f [.,.] h [.,.] Image filtering
g[ ,]
Credit: S. Seitz
? [ , ] [ , ] [ , ] , h m n g k l f m k n l k l
Docsity.com
f [.,.] h [.,.] Image filtering
g[ , ]
Credit: S. Seitz
[ , ] [ , ] [ , ] , h m n g k l f m k n l k l
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What does it do?
- Replaces each pixel with an average of its neighborhood
- Achieve smoothing effect (remove sharp features) 1 1 1 1 1 1 1 1 1 Slide credit: David Lowe (UBC) g[ ,] Box Filter
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Practice with linear filters
0 0 0 0 1 0 0 0 0 Original
Source: D. LoweDocsity.com
Practice with linear filters 0 0 0 0 1 0 0 0 0 Original (^) Filtered (no change)
Source: D. LoweDocsity.com
Practice with linear filters 0 0 0 0 0 1 0 0 0 Original (^) Shifted left By 1 pixel
Source: D. LoweDocsity.com
Practice with linear filters
Original 1 1 1 1 1 1 1 1 1 0 0 0 0 2 0 0 0 0
(Note that filter sums to 1)