lec6-prelim

# lec6-prelim - Lecture 6 HW1 due today Last Class...

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USC CS574: Computer Vision, Fall 2010 Copyright 2010, by R. Nevatia 1 Lecture 6: Sept 13, 10 HW1 due today Last Class Segmentation methods: histogram based, k-means clustering Intro to mean shift filtering Today’s objective Tutorial on OpenCV Mean shift segmentation Normalized cuts

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USC CS574: Computer Vision, Fall 2010 Copyright 2010, by R. Nevatia 2 Mean Shift Filtering From work of Comaniciu and Meer (two papers posted on the class web page); also see RS, Section 5.3.2 Filtering while preserving regions/edges General idea is to estimate the maxima of the probability density function given only some sample points (drawn according to the density function) Where are the clusters figure (b), RS fig 5.16
USC CS574: Computer Vision, Fall 2010 Copyright 2010, by R. Nevatia 3 Kernel Density Estimation (RS 5.3.2) f(x) is the estimate, K(x) is the kernel function, G(x) is derivative of K(x) Direct computation can be expensive, instead, compute maxima using the mean-shift method

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USC CS574: Computer Vision, Fall 2010 Copyright 2010, by R. Nevatia 4 Mean Shift Filtering General idea Choose a neighborhood Average over points in the neighborhood (may be weighted by a Gaussian or some other kernel function) to compute a mean Shift center of neighborhood to new mean (hence mean shift ) Repeat until convergence (guaranteed with proper choice of kernel and shift steps) Replace range of point with that of convergent point Can be shown that the procedure results in estimating local maximum of f(x) Consider all points converging to the same maximum to correspond to the same cluster, assign them the value of the maximum (for filtering)
USC CS574: Computer Vision, Fall 2010 Copyright 2010, by R. Nevatia 5 Example Binary function in 2-D

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USC CS574: Computer Vision, Fall 2010 Copyright 2010, by R. Nevatia
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lec6-prelim - Lecture 6 HW1 due today Last Class...

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