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Feature

REALGraphics

Filter By Convolution

Issue: 7.3 (March/April 2009)
Author: JC Cruz
Article Description: No description available.
Article Length (in bytes): 20,236
Starting Page Number: 31
RBD Number: 7311
Resource File(s):

Download Icon 7311.zip Updated: Monday, March 2, 2009 at 1:37 PM

Related Web Link(s):

http://www.apolloarchive.com/apollo_gallery.html
http://www.dfanning.com/ip_tips/sharpen.html
http://www.generation5.org/content/2004/noiseIntro.asp
http://en.wikipedia.org/wiki/Edge_detection
http://en.wikipedia.org/wiki/Matrix_(mathematics)
http://en.wikipedia.org/wiki/Noise_reduction
http://www.ph.tn.tudelft.nl/Courses/FIP/noframes/fip-Convolut-2.html

Known Limitations: None

Excerpt of article text...

Welcome back to another installment of REALGraphics. Today's topic is the concept of convolution filters. First, we learn the concept of convolution and how it is used to filter image data. Then we learn the parts that make up a convolution filter. Next, we look at some examples of convolution filters. We then build and test a basic filter as a finite-state machine.

The Convolution Filter

The convolution filter is a feature found in many high-end image processing software programs. It belongs to a family of matrix algorithms. The filter works by first dividing the image data into small, equal-sized regions. To each region, it applies a set of constants by a process called convolution. The filter then uses the results to build a modified image.

Anatomy of a filter

There are three parts that make up a convolution filter (see Figure 1). First, there is the sampler. Its function, as you have deduced, is to take regular samples of the target image. Each sample consists of the target pixel and its neighbors, all arranged as a matrix (see sidebar). How the sampling process works is explained later on.

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Article copyrighted by REALbasic Developer magazine. All rights reserved.


 


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