NDLI: Image sets for the training of image processing systems

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AOAB: automated optimization algorithm benchmarking

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Image sets for the training of image processing systems

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Genetic programming theory

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Experimental optimization by evolutionary algorithms

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A fitness-based adaptation scheme for control parameters in differential evolution

Statistical analysis for evolutionary computation: introduction

Statistical analysis for evolutionary computation: advanced techniques

Cartesian genetic programming

Mirrored variants of the (1,2)-CMA-ES compared on the noiseless BBOB-2010 testbed

Constraint handling with modified hypervolume indicator for multi-objective optimization problems

Robot phylogenetics

Evolving neural networks

Computational complexity and evolutionary computation

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Mirrored variants of the (1,2)-CMA-ES compared on the noisy BBOB-2010 testbed

LESR CLASS: an LCS for securities trading rulesets

Mirrored variants of the (1,4)-CMA-ES compared on the noisy BBOB-2010 testbed

Dimensionality reduction using symbolic regression

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Consultant-guided search combined with local search for the traveling salesman problem

Investigating the impact of sequential selection in the (1,4)-CMA-ES on the noiseless BBOB-2010 testbed

Consultant-guided search algorithms for the quadratic assignment problem

Investigating the impact of sequential selection in the (1,2)-CMA-ES on the noisy BBOB-2010 testbed

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Investigating the impact of sequential selection in the (1,4)-CMA-ES on the noisy BBOB-2010 testbed

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Benchmarking the (1,4)-CMA-ES with mirrored sampling and sequential selection on the noiseless BBOB-2010 testbed

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Benchmarking the (1,4)-CMA-ES with mirrored sampling and sequential selection on the noisy BBOB-2010 testbed

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Benchmarking CMA-EGS on the BBOB 2010 noiseless function testbed

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Benchmarking CMA-EGS on the BBOB 2010 noisy function testbed

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Benchmarking a MOS-based algorithm on the BBOB-2010 noiseless function testbed

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Benchmarking SPSA on BBOB-2010 noisy function testbed

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Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noisy testbed

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Fuzzy social interaction genetic algorithm

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Genetic algorithm with multi-colony dynamic reproduction

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Fitness importance for online evolution

Niching the CMA-ES via nearest-better clustering

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Black-box optimization benchmarking for noiseless function testbed using artificial bee colony algorithm

Benchmarking a MOS-based algorithm on the BBOB-2010 noisy function testbed

Real-coded genetic algorithm benchmarked on noiseless black-box optimization testbed

Benchmarking real-coded genetic algorithm on noisy black-box optimization testbed

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Comparison of G3PCX and Rosenbrock's algorithms on the BBOB noiseless testbed

Image sets for the training of image processing systems

Content Provider	ACM Digital Library
Author	Peterson, Michael Aldridge, Shawn Herzog, Britny
Abstract	State of the Art image compression algorithms utilize Discrete Wavelet Transforms (DWTs), to losslessly compress raw images for storage and transmission. These techniques form the basis for image formats such as JPEG2000, as well as the storage of medical images, such as ultrasound and CT scans. The transmission of defense and aerospace images, such as those taken by unmanned aerial vehicles (UAVs), and satellites, is also built upon these techniques. Recent research has shown that image compression filters can be optimized through the use of evolutionary algorithms (EAs). The images used to train these optimized EAs must be chosen to reflect the likely applications of the image processing system. This paper presents a set of 50 images, for use as training images for UAV and satellite image processing systems. The image set is taken from a diverse range of satellite images, including airports, cities, and military bases considered representative of likely targets for reconnaissance missions. We first evolve filters using each image, and then identify the most effective training images based on average mean squared error (MSE) improvement over existing wavelets when each filter is applied across the entire set of images. The resulting subset of images will provide a starting point for the evolution of other defense application image pro.
Starting Page	2039
Ending Page	2042
Page Count	4
File Format	PDF
ISBN	9781450300735
DOI	10.1145/1830761.1830855
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Satellite imagery Wavelets Image processing Evolutionary computation Genetic algorithms
Content Type	Text
Resource Type	Article

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