CSE527-l7

CSE527-l7 - Feature Extraction Features local meaningful...

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1 Feature Extraction • Features: local meaningful detectable • Points • Edges – Step edges – Line edges – Contours – Deformable models • Closed contours are boundaries • Regions 2 Gabor filterbanks: consider combinations of and Spatially localized frequency analysis Increasing Previous Lecture: Gabor filters Horizontal Frequency Vertical Frequency Fourier transform isocurves Increasing

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2 Envelope estimation for a 2D sinusoidal 3 Convolve Textons (Malik et al, IJCV 2001) Filterbank Image Database
3 ….. frequency codewords Object recognition as texture classification Pattern recognition task

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4 Template Matching • The most straightforward approach to finding a particular pattern in the image is to look at every patch of pixels and to directly compare the intensity values in that patch to the intensity values in the search pattern. • Consider two vectors of intensity values, x and y, One way to compare them is by simply computing the Sum of Squared Differences (SSD) Line Fitting • Follows edge extraction and linking • Used to find straight line features in an image • Simplest way split and merge techniques – Problem with corners
5 Parameterizing lines • For our purposes lines in the image will be parameterized by a vector of 3 numbers (m x , m y , m z ) where: m x 2 + m y 2 = 1 • Points on the line are defined by the equation: m x *x + m y *y + m z = 0 m z (m x , m y ) Least Squares Fitting • Given a set of points (xi, yi) the goal of the fitting procedure is to find the parameters (m x , m y , m z ) which represent the best line

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6 Least squares criterion • The best fit is defined in terms of the parameters which minimize the sum of the squared residuals Steps • Compute the centroid of the point set • Change coordinates such that the new centroid is (0,0)
7 Steps (II) • Solve for (m x , m y ) by minimizing the following quadratic form • with e m m m x m y x y x i y i i ( , ) ( ) = + 2 Steps (III) • Solve for m z

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8 Issues • The main problem with least squares fitting techniques is that they are heavily influenced by outliers • Solutions to this problem include – Robust weighting measures – Iteratively reweighted least squares – Least median squares The Hough Transform • Another approach that can be used to detect features such as lines in an image is the Hough transform • This approach is a voting scheme based on accumulating evidence in a parameter space
9 The Hough Transform

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CSE527-l7 - Feature Extraction Features local meaningful...

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