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<br /> <b>Notice</b>: Undefined variable: noHierarchyInTitle in <b>/var/www/</b> on line <b>17</b><br /> Toronto, Neil's Publications (detailed list) - NNML Laboratory - BYU CS Department
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  Toronto, Neil's Publications (detailed list)

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This page contains the titles and abstracts of papers written by author Toronto, Neil, a member of the BYU Neural Networks and Machine Learning (NNML) Research Group. Postscript files are available for most papers. A more concise list is available.

To view the entire list in one page, click here.

Super-Resolution via Recapture and Bayesian Effect Modeling

  • Authors: Neil Toronto and Bryan Morse and Kevin Seppi and Dan Ventura
  • Abstract: This paper presents Bayesian edge inference (BEI), a single-frame super-resolution method explicitly grounded in Bayesian inference that addresses issues common to existing methods. Though the best give excellent results at modest magnification factors, they suffer from gradient stepping and boundary coherence problems by factors of 4x. Central to BEI is a causal framework that allows image capture and recapture to be modeled differently, a principled way of undoing downsampling blur, and a technique for incorporating Markov random field potentials arbitrarily into Bayesian networks. Besides addressing gradient and boundary issues, BEI is shown to be competitive with existing methods on published correctness measures. The model and framework are shown to generalize to other reconstruction tasks by demonstrating BEI’s effectiveness at CCD demosaicing and inpainting with only trivial changes.
  • Reference: In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, page to appear, June 2009.
  • BibTeX
  • Download the file: pdf

The Hough Transform’s Implicit Bayesian Foundation

  • Authors: Neil Toronto and Bryan Morse and Dan Ventura and Kevin Seppi
  • Abstract: This paper shows that the basic Hough transform is implicitly a Bayesian process—that it computes an unnormalized posterior distribution over the parameters of a single shape given feature points. The proof motivates a purely Bayesian approach to the problem of finding parameterized shapes in digital images. A proof-of-concept implementation that finds multiple shapes of four parameters is presented. Extensions to the basic model that are made more obvious by the presented reformulation are discussed.
  • Reference: In Proceedings of the IEEE International Conference on Image Processing, pages 377–380, September 2007.
  • BibTeX
  • Download the file: pdf

Learning Quantum Operators From Quantum State Pairs

  • Authors: Neil Toronto and Dan Ventura
  • Abstract: Developing quantum algorithms has proven to be very difficult. In this paper, the concept of using classical machine learning techniques to derive quantum operators from examples is presented. A gradient descent algorithm for learning unitary operators from quantum state pairs is developed as a starting point to aid in developing quantum algorithms. The algorithm is used to learn the quantum Fourier transform, an underconstrained two-bit function, and Grover’s iterate.
  • Reference: In IEEE World Congress on Computational Intelligence, pages 2607–2612, July 2006.
  • BibTeX
  • Download the file: pdf

Edge Inference for Image Interpolation

  • Authors: Neil Toronto and Dan Ventura and Bryan S. Morse
  • Abstract: Image interpolation algorithms try to fit a function to a matrix of samples in a "natural-looking" way. This paper presents edge inference, an algorthm that does this by mixing neural network regression with standard image interpolation techniques. Results on gray level images are presented. Extension into RGB color space and additional applications of the algorithm are discussed.
  • Reference: In International Joint Conference on Neural Networks, pages 1782–1787, 2005.
  • BibTeX
  • Download the file: pdf

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