Presentation on theme: "Slides from Alexei Efros"— Presentation transcript:
1Slides from Alexei Efros Gaussian PyramidSlides from Alexei Efros
2Sampling Good sampling: Bad sampling: Sample often or, Sample wisely see aliasing in action!
3Gaussian pre-filtering Solution: filter the image, then subsampleFilter size should double for each ½ size reduction. Why?
4Subsampling with Gaussian pre-filtering Solution: filter the image, then subsampleFilter size should double for each ½ size reduction. Why?How can we speed this up?
5Compare with... 1/2 1/4 (2x zoom) 1/8 (4x zoom) Why does this look so crufty?
6Image Pyramids Known as a Gaussian Pyramid [Burt and Adelson, 1983] In computer graphics, a mip map [Williams, 1983]A precursor to wavelet transform
7A bar in the big images is a hair on the zebra’s nose; in smaller images, a stripe; in the smallest, the animal’s noseFigure from David Forsyth
8Gaussian pyramid construction filter maskRepeatFilterSubsampleUntil minimum resolution reachedcan specify desired number of levels (e.g., 3-level pyramid)The whole pyramid is only 4/3 the size of the original image!
9Gaussian pyramid construction is similar to Gaussian
10Laplacian Pyramid Laplacian Pyramid decomposition Gaussian Pyramid Created from Gaussian pyramid by subtraction
11Laplacian Pyramid Laplacian Pyramid decomposition Gaussian Pyramid Created from Gaussian pyramid by subtraction
12What are they good for? Improve Search Precomputation Image Processing Search over translationsLike homeworkClassic coarse-to-fine stategySearch over scaleTemplate matchingE.g. find a face at different scalesPrecomputationNeed to access image at different blur levelsUseful for texture mapping at different resolutions (called mip-mapping)Image ProcessingEditing frequency bands separetlyE.g. image blending… next time!