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

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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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