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L8-9+Notes - Try a lot of different weights under...

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Barrett - CS450 Winter 2011 Lecture 8-9 Spatial filtering Objectives : To understand the use of convolution and filter masks to implement a variety of spatial filters. See accompanying powerpoint slides. Also, as an excellent supplemental resource on Image Arithmetic see: http://homepages.inf.ed.ac.uk/rbf/HIPR2/filtops.htm 1. A running average is just a simple filter: Signal: 2 6 4 6 8 1 0 5 4 3-point average: 4 5 6 5 3 2 3 2. A weighted average is also just a simple filter: Signal: 2 6 4 6 8 1 0 5 4 3-point weighted average: 4.5 5 5 5.75 2.5 1.5 2.25 (weights: .25, .5, .25) 3. Both Running and Weighted Averages can be implemented as a Convolution So what’s a convolution? Just what we did – multiply-add-sum (Ratchet analogy) Strictly speaking, convolution means reverse weights before multiplying 4. Adobe Photoshop Demo with input of different weights
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Unformatted text preview: Try a lot of different weights under Filters > Custom (with and without scaling and bias) • Smoothing with Uniform weighting (box filter) • Smoothing with Gaussian weighting – with radius of different sizes - How can this do a 100x100 Gaussian Smooth so quickly? (Separability into 2 1-D smooths) • Edge Detection (Horizontal, Vertical) – then combine (with Opacity) to illustrate Gradient Magnitude • Laplacian • Sharpening 5. Median filter Zoom image up – create white pixel, black pixel over small ROI – right over the top of an edge. Then use Median Filter to clean it up without destroying the edge. 6. Unsharp Masking Reverse-engineer the Unsharp Masking Kernel. 7. The Laplacian Operator Derive the Laplacian Operator as the difference of the differences....
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