2D Minimum and Maximum Filters: Algorithms and Implementation Issues

24 January 2011 in Algorithms, C++, Image Processing | 2 comments

A while back I needed to implement fast minimum and maximum filters for images. I devised (what I thought was) a clever approximation scheme where the execution time is not dependent on the window size of the filter. But the method had some issues, and I looked at some other algorithms. In retrospect, the method I used seems foolish. At the time, I did not realise the obvious: a 1D filter could be applied to first the rows, and then the columns of an image, which makes the slow algorithm faster, or allows you to use one of the many published fast 1D algorithms.

I wanted to write down my gained knowledge, and started to work on a blog post. But soon it became quite long, so I decided to put it into a PDF document instead. You can download it below.

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This is a substantial update of this reference document. The most important addition is the chain and substitution rules for arithmetic difference calculus (ADC). Other additions include: more properties of the discrete power function, more properties of ADC operators, definitions of analog functions, and ranges of convergence of (some) z-transforms. I also corrected some errors that were discovered since the last version.

Grab it here.

30 September 2010 | No comments

I have updated the Reference for Functional Equations. I have added several entries to the tables, updated the graphs, added some new graphs, added some explanations and additional notes on notation, corrected a few typos, and re-organised the document slightly. Get the new version here.

25 August 2010 | No comments

A Simple Trick for Moving Objects in a Physics Simulation

27 July 2010 in Algorithms, Game Development, Simulation | 2 comments

(Original Image by Valerie Everett)

It is sometime necessary to move an object in a physics simulation to a specific point. On the one hand, it can be difficult to analyse the exact force you have to apply; on the other hand it might not look so good if you animate the object’s position directly.

A compromise that works well in many situations is to use a spring-damper system to move the object.

The trick is simple: we apply two forces—the one is proportional to the displacement; the other is proportional to the velocity. Tweaked correctly, they combine to give realistic movement to the desired point.

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Region Quadtrees in C++

15 June 2010 in Algorithms, C++, Downloads, Game Development, Image Processing | No comments

quadtree

(Original image by GoAwayStupidAI).

Below are four C++ implementations of the region quadtree (the kind used for image compression, for example). The different implementations were made in an attempt to optimise construction of quadtrees. (For a tutorial on implementing region quadtrees, see Issue 26 [6.39 MB zip] of Dev.Mag).

  • NaiveQuadtree is the straightforward implementation.
  • AreaSumTableQuadtree uses a summed area table to perform fast calculations of the mean and variance of regions in the data grid.
  • AugmentedAreaSumTableQuadtree is the same, except that the area sum table has an extra row and column of zeros to prevents if-then logic that slows it down and makes it tricky to understand.
  • SimpleQuadtree is the same as AugmentedAreaSumTableQuadtree , except that no distinction is made (at a class level) between different node types.

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Catching Common Image Processing Programming Errors with Generic Unit Tests

12 May 2010 in Algorithms, Image Processing | No comments

crash test dummy

When implementing image algorithms, I am prone to make these mistakes:

  • swapping x and y;
  • working on the wrong channel;
  • making off-by-one errors, especially in window algorithms;
  • making division-by-zero errors;
  • handling borders incorrectly; and
  • handling non-power-of-two sized images incorrectly.

Since these types of errors are prevalent in many image-processing algorithms, it would be useful to develop, once and for all, general tests that will catch these errors quickly for any algorithm.

This post is about such tests.

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Simple, Fast* Approximate Minimum / Maximum Filters

16 April 2010 in Algorithms, Image Processing | 2 comments

minmax

*Fast = not toooo slow…

For the image restoration tool I had to implement min and max filters (also erosion and dilation—in this case with a square structuring element). Implementing these efficiently is not so easy. The naive approach is to simply check all the pixels in the window, and select the maximum or minimum value. This algorithm’s run time is quadratic in the window width, which can be a bit slow for the bigger windows that I am interested in. There are some very efficient algorithms available, but they are quite complicated to implement properly (some require esoteric data structures, for example monotonic wedges (PDF)), and many are not suitable for floating point images.

So I came up with this approximation scheme. It uses some expensive floating point operations, but its run time is constant in the window width.

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Experimental Tool for Removing Unwanted Artefacts in Textures

15 April 2010 in Downloads, Game Development, Image Processing, Tools | 1 comment

texture

Many textures used for 3D art start from photographs. Ideally, such textures should be uniformly lit so that the texture does not interfere with the lighting applied by the 3D software. Often, lighting artefacts must be removed by hand. This can be tedious and time consuming.

The tool provided here aims to automate this process. It is still in an experimental phase, so it is very crude. Below you can see some of the before and after pictures.

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poissonI decided to put the Poisson disk sampling code here for download since the site that hosted it is down. The code accompanies the tutorial on Dev.Mag: Poisson Disk Sampling.

Download

poisson_disk_java.zip (184 KB)
poisson_disk_python.zip (912 KB)
poisson_disk_ruby.zip (59 KB)

7 April 2010 | No comments

Tips for Designing and Implementing a Stimulus Response Agent

25 October 2009 in Game Development | No comments

brain
(Original Image by everyone’s idle.)

This post was a originally published on Luma Labs, now dead.

As old as stimulus-response techniques are, they still form an important part of many AI systems, even if it is a thin layer underneath a sophisticated decision, planning, or learning system. In this tutorial I give some advice to their design and implementation, mostly out of experience gained from implementing the AI for some racing games.

A stimulus response agent (or a reactive agent) is an agent that takes inputs from its world through sensors, and then takes action based on those inputs through actuators. Between the stimulus and response, there is a processing unit that can be arbitrarily complex. An example of such an agent is one that controls a vehicle in a racing game: the agent “looks” at the road and nearby vehicles, and then decides how much to turn and break.

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