Amy Tabb, “bib test,” Amy Tabb. [Online]. Available at: https://amytabb.com/tips/2023/02/04/bib-test/. [Accessed: February 15, 2023]
Lots of stuff
Lots of stuff
[1] R. Barrett et al., Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, 2nd Edition. Philadelphia, PA: SIAM, 1994.
@book{templates,
author = {Barrett, R. and Berry, M. and Chan, T. F. and Demmel, J. and Donato, J. and Dongarra, J. and Eijkhout, V. and Pozo, R. and Romine, C. and der Vorst, H. Van},
title = {Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, 2nd Edition},
publisher = {SIAM},
year = {1994},
address = {Philadelphia, PA},
isbn = {9780898713282},
freepdf = {https://netlib.org/templates/templates.pdf}
}
[2] W. Chojnacki, M.J. Brooks, A. van den Hengel, and D. Gawley, “Revisiting Hartley’s normalized eight-point algorithm,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1172–1177, Sep. 2003, doi: 10.1109/TPAMI.2003.1227992. [Online]. Available at: http://ieeexplore.ieee.org/document/1227992/. [Accessed: February 7, 2023]
@article{chojnacki_revisiting_2003,
title = {Revisiting Hartley's normalized eight-point algorithm},
volume = {25},
issn = {0162-8828},
url = {http://ieeexplore.ieee.org/document/1227992/},
doi = {10.1109/TPAMI.2003.1227992},
language = {en},
number = {9},
urldate = {2023-02-07},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
author = {Chojnacki, W. and Brooks, M.J. and van den Hengel, A. and Gawley, D.},
month = sep,
year = {2003},
pages = {1172--1177},
freepdf = {https://cs.adelaide.edu.au/users/mjb/Papers/revising_pami03.pdf}
}
Hartley’s eight-point algorithm has maintained an important place in computer vision, notably as a means of providing an initial value of the fundamental matrix for use in iterative estimation methods. In this paper, a novel explanation is given for the improvement in performance of the eight-point algorithm that results from using normalized data. It is first established that the normalized algorithm acts to minimize a specific cost function. It is then shown that this cost function I!; statistically better founded than the cost function associated with the nonnormalized algorithm. This augments the original argument that improved performance is due to the better conditioning of a pivotal matrix. Experimental results are given that support the adopted approach. This work continues a wider effort to place a variety of estimation techniques within a coherent framework.
[3] R.I. Hartley, “In defense of the eight-point algorithm,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 6, pp. 580–593, Jun. 1997, doi: 10.1109/34.601246. [Online]. Available at: http://ieeexplore.ieee.org/document/601246/. [Accessed: February 7, 2023]
@article{hartley_defense_1997,
title = {In defense of the eight-point algorithm},
volume = {19},
issn = {01628828},
url = {http://ieeexplore.ieee.org/document/601246/},
doi = {10.1109/34.601246},
language = {en},
number = {6},
urldate = {2023-02-07},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
author = {Hartley, R.I.},
month = jun,
year = {1997},
pages = {580--593},
freepdf = {https://courses.nus.edu.sg/course/eleclf/ee4212/hartley_defense_8_point.pdf}
}
The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras, and the eight-point algorithm is a frequently cited method for computing the fundamental matrix from a set of eight or more point matches. It has the advantage of simplicity of implementation. The prevailing view is, however, that it is extremely susceptible to noise and hence virtually useless for most purposes. This paper challenges that view, by showing that by preceding the algorithm with a very simple normalization (translation and scaling) of the coordinates of the matched points, results are obtained comparable with the best iterative algorithms. This improved performance is justified by theory and verified by extensive experiments on real images.
[4] Lloyd N. Trefethen and David Bau, Numerical Linear Algebra. SIAM, 1997.
@book{trefethen97,
added-at = {2010-09-19T02:35:23.000+0200},
author = {Trefethen, Lloyd N. and Bau, David},
biburl = {https://www.bibsonomy.org/bibtex/2e45a2ed5ccc6dc12721cde613217c222/ytyoun},
interhash = {1e7e7a44cbff3092be50a71fe056c8ec},
intrahash = {e45a2ed5ccc6dc12721cde613217c222},
isbn = {978-1611977158},
keywords = {characteristic eigenvalues linear.algebra matrix numerical numerical.analysis polynomial secular.equation textbook},
publisher = {SIAM},
timestamp = {2017-11-25T07:18:16.000+0100},
title = {Numerical Linear Algebra},
year = {1997},
tldr = {Numerical Analysis; I found this textbook clear and inviting. Contains the core methods of numerical linear algebra: SVD, decompositions, etc. Originally published in 1997, and republished in 2022.},
doi = {10.1137/1.9781611977165}
}