poster.bib

@inproceedings{villiers_centi-pixel_2008,
  title     = {Centi-pixel accurate real-time inverse distortion correction},
  volume    = {7266},
  url       = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/7266/726611/Centi-pixel-accurate-real-time-inverse-distortion-correction/10.1117/12.804771.full},
  doi       = {10.1117/12.804771},
  abstract  = {Inverse distortion is used to create an undistorted image from a distorted image. For each pixel in the undistorted image it is required to determine which pixel in the distorted image should be used. However the process of characterizing a lens using a model such as that of Brown, yields a non-invertible mapping from the distorted domain to the undistorted domain. There are three current approaches to solving this: an approximation of the inverse distortion is derived from a low-order version of Brown's model; an initial guess for the distorted position is iteratively refined until it yields the desired undistorted pixel position; or a look-up table is generated to store the mapping. Each approach requires one to sacrifice either accuracy, memory usage or processing time. This paper shows that it is possible to have real-time, low memory, accurate inverse distortion correction. A novel method based on the re-use of left-over distortion characterization data is combined with modern numerical optimization techniques to fit a high-order version of Brown's model to characterize the inverse distortion. Experimental results show that, for thirty-two 5mm lenses exhibiting extreme barrel distortion, inverse distortion can be improved 25 fold to 0.013 pixels RMS over the image.},
  urldate   = {2023-02-06},
  booktitle = {Optomechatronic {Technologies} 2008},
  publisher = {SPIE},
  author    = {Villiers, Jason P. de and Leuschner, F. Wilhelm and Geldenhuys, Ronelle},
  month     = nov,
  year      = {2008},
  pages     = {320--327}
}

@article{drap_exact_2016,
  title     = {An {Exact} {Formula} for {Calculating} {Inverse} {Radial} {Lens} {Distortions}},
  volume    = {16},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  issn      = {1424-8220},
  url       = {https://www.mdpi.com/1424-8220/16/6/807},
  doi       = {10.3390/s16060807},
  abstract  = {This article presents a new approach to calculating the inverse of radial distortions. The method presented here provides a model of reverse radial distortion, currently modeled by a polynomial expression, that proposes another polynomial expression where the new coefficients are a function of the original ones. After describing the state of the art, the proposed method is developed. It is based on a formal calculus involving a power series used to deduce a recursive formula for the new coefficients. We present several implementations of this method and describe the experiments conducted to assess the validity of the new approach. Such an approach, non-iterative, using another polynomial expression, able to be deduced from the first one, can actually be interesting in terms of performance, reuse of existing software, or bridging between different existing software tools that do not consider distortion from the same point of view.},
  language  = {en},
  number    = {6},
  urldate   = {2023-02-08},
  journal   = {Sensors},
  author    = {Drap, Pierre and Lefèvre, Julien},
  month     = jun,
  year      = {2016},
  note      = {Number: 6
               Publisher: Multidisciplinary Digital Publishing Institute},
  keywords  = {distortion correction, power series, radial distortion},
  pages     = {807}
}

@misc{andrychowicz_learning_2016,
  title     = {Learning to learn by gradient descent by gradient descent},
  url       = {http://arxiv.org/abs/1606.04474},
  abstract  = {The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way. Our learned algorithms, implemented by LSTMs, outperform generic, hand-designed competitors on the tasks for which they are trained, and also generalize well to new tasks with similar structure. We demonstrate this on a number of tasks, including simple convex problems, training neural networks, and styling images with neural art.},
  language  = {en},
  urldate   = {2023-03-15},
  publisher = {arXiv},
  author    = {Andrychowicz, Marcin and Denil, Misha and Gomez, Sergio and Hoffman, Matthew W. and Pfau, David and Schaul, Tom and Shillingford, Brendan and de Freitas, Nando},
  month     = nov,
  year      = {2016},
  note      = {arXiv:1606.04474 [cs]},
  keywords  = {Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing}
}

@misc{mathworks_innovation,
  title        = {MathWorks Excellence in Innovation: Applying Machine Learning for the Development of Physical Sensor Models in Game Engine Environment},
  url          = {https://github.com/mathworks/MathWorks-Excellence-in-Innovation},
  abstractnote = {Capstone and senior design project ideas for undergraduate and graduate students to gain practical experience and insight into technology trends and industry directions. - MathWorks-Excellence-in-I...},
  journal      = {GitHub},
  language     = {en},
  note         = {https://github.com/mathworks/MathWorks-Excellence-in-Innovation}
}