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This page and its subtopics discusses everything about Artificial Neural Networks.
 
This page and its subtopics discusses everything about Artificial Neural Networks.
  
Lectures discussing Artificial Neural Networks: [[Lecture 13_OldKiwi]] and [[Lecture 14_OldKiwi]]
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Lectures discussing Artificial Neural Networks: [[Lecture_13_-_Kernel_function_for_SVMs_and_ANNs_introduction_OldKiwi|Lecture 13, ECE662 Spring 2010]] and [[Lecture_14_-_ANNs%2C_Non-parametric_Density_Estimation_(Parzen_Window)_OldKiwi|Lecture 14, ECE662, Spring 2010]]
  
Helpful in-depth lecture slides regarding ANN: [http://www.ir.iit.edu/~nazli/cs422/CS422-Slides/DM-NeuralNetwork.pdf link]
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Relevant homework: [[Homework 2_OldKiwi]]
  
 
== References ==
 
== References ==
  
* http://en.wikipedia.org/wiki/Neural_network
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* [http://www.ir.iit.edu/~nazli/cs422/CS422-Slides/DM-NeuralNetwork.pdf In-depth Lecture Slides]
* http://www.sciencedirect.com/science/journal/08936080
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* [http://en.wikipedia.org/wiki/Neural_network Neural Network on Wikipedia]
* http://en.wikipedia.org/wiki/Artificial_neural_network
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* [http://www.sciencedirect.com/science/journal/08936080 Special Issue on Advances in Neural Networks Research: IJCNN’07]
* http://uhaweb.hartford.edu/compsci/neural-networks-tutorial.html
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* [http://en.wikipedia.org/wiki/Artificial_neural_network Artificial Neural Network on Wikipedia]
* http://www.mathworks.com/products/demos/nnettlbx/probabilistic/
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* [http://uhaweb.hartford.edu/compsci/neural-networks-tutorial.html Neural Network Tutorial]
* http://www.dsi.unifi.it/ANNPR/CR/23.pdf
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* [http://www.mathworks.com/products/demos/nnettlbx/probabilistic/ MATLAB Neural Network Toolbox]
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* [http://www.dsi.unifi.it/ANNPR/CR/23.pdf Probabilistic Neural Network]
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* An Implementation of Neural Network: [http://www.rgu.ac.uk/files/chapter3%20-%20bp.pdf Back Propagation Algorithm]
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== Application of Neural Networks to Color Calibration ==
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The below is a link to a paper which employs Neural Network in calibrating scanner.
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* [https://ritdml.rit.edu/dspace/bitstream/1850/3035/1/PAndersonArticle04-01-1992.pdf]
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Summary: The sanner calibrtion has largely two procedures, gray balancing and transfrom linear RGB data to device independent XYX data. The purpose of this paper is to improve the performance of transformation from RGB to XYZ. The traditional method to transfrom linear RGB to XYZ is to find 3x3 linear transform matrix by minimizing the perceptual error. The author argue that by using Neural Network more precise transform form linear RGB to XYZ can be achieved, as expected, since Neural Network provide more complex nonlinear transformation from input and output. He measuerd the transform error in perceptually uniform domain, and prove the strength of Neural Network in scanner calibration process.
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[[Category:ECE662]]

Latest revision as of 10:46, 16 April 2010

This page and its subtopics discusses everything about Artificial Neural Networks.

Lectures discussing Artificial Neural Networks: Lecture 13, ECE662 Spring 2010 and Lecture 14, ECE662, Spring 2010

Relevant homework: Homework 2_OldKiwi

References


Application of Neural Networks to Color Calibration

The below is a link to a paper which employs Neural Network in calibrating scanner.

Summary: The sanner calibrtion has largely two procedures, gray balancing and transfrom linear RGB data to device independent XYX data. The purpose of this paper is to improve the performance of transformation from RGB to XYZ. The traditional method to transfrom linear RGB to XYZ is to find 3x3 linear transform matrix by minimizing the perceptual error. The author argue that by using Neural Network more precise transform form linear RGB to XYZ can be achieved, as expected, since Neural Network provide more complex nonlinear transformation from input and output. He measuerd the transform error in perceptually uniform domain, and prove the strength of Neural Network in scanner calibration process.

Alumni Liaison

Sees the importance of signal filtering in medical imaging

Dhruv Lamba, BSEE2010