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Books on the topic 'Image processing Mathematical statistics'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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1

service), SpringerLink (Online, ed. Mathematical SETI: Statistics, Signal Processing, Space Missions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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2

Hyvärinen, Aapo. Natural Image Statistics: A Probabilistic Approach to Early Computational Vision. London: Springer London, 2009.

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3

Aschwanden, Peter Franz. Experimenteller Vergleich von Korrelationskriterien in der Bildanalyse. Konstanz: Hartung-Gorre, 1993.

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4

Image analysis, random fields, and dynamic Monte Carlo methods: A mathematical introduction. Berlin: Springer-Verlag, 1995.

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5

Bajorski, Peter. Statistics for imaging, optics, and photonics. Hoboken, N.J: Wiley, 2012.

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6

Gerhard, Winkler. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2nd ed. Berlin: Springer, 2003.

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7

1946-, Winkler Gerhard, ed. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2nd ed. Berlin: Springer, 2003.

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8

B, Desai Uday, ed. Bayesian approach to image interpretation. Boston: Kluwer Academic Publishers, 2001.

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9

Weinert, Howard L. Fast Compact Algorithms and Software for Spline Smoothing. New York, NY: Springer New York, 2013.

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10

Bergounioux, Maïtine, ed. Mathematical Image Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19604-1.

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11

Bredies, Kristian, and Dirk Lorenz. Mathematical Image Processing. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01458-2.

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12

Dougherty, Edward R., and Jaakko Astola, eds. Mathematical Nonlinear Image Processing. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3148-7.

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13

J, Twining Carole, and Taylor Chris J, eds. Statistical models of shape: Optimisation and evaluation. London: Springer, 2008.

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14

Goudail, Franois. Statistical image processing techniques for noisy images: An application-oriented approach. New York, NY: Kluwer Academic / Plenum Publishers, 2003.

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15

Chidume, Charles E. Mathematical problems in image processing. Edited by Abdus Salam International Centre for Theoretical Physics. Trieste, Italy: ICTP--The Abdus Salam International Centre for Theoretical Physics, 2000.

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16

Aubert, Gilles, and Pierre Kornprobst. Mathematical Problems in Image Processing. New York, NY: Springer New York, 2006. http://dx.doi.org/10.1007/978-0-387-44588-5.

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17

Aubert, Gilles, and Pierre Kornprobst. Mathematical Problems in Image Processing. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/b97428.

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18

Mathematical morphology. London: ISTE, 2010.

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19

Petra, Perner, Brause Rüdiger W, and Holzhütter Hermann-Georg, eds. Medical data analysis: 4th International Symposium, ISMDA 2003, Berlin, Germany, October 9-10, 2002 : proceedings. Berlin: Springer, 2003.

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20

M, Blackledge J., and Institute of Mathematics and Its Applications., eds. Image processing: Mathematical methods and applications. Oxford: Clarendon Press, 1997.

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21

P, Tsokos Chris, ed. Mathematical statistics with applications. Amsterdam: Academic Press, 2009.

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22

Moser, Gabriele, and Josiane Zerubia, eds. Mathematical Models for Remote Sensing Image Processing. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-66330-2.

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23

Serra, Jean. Mathematical Morphology and Its Applications to Image Processing. Dordrecht: Springer Netherlands, 1994.

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24

Lenz, Reiner. Group theoretical methods in image processing. Berlin: Springer-Verlag, 1990.

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25

Image processing and mathematical morphology fundamentals and applications. Boca Raton: Taylor & Francis, 2009.

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26

IMA Conference on Image Processing : Mathematical Methods, Algorithms and Applications (3rd 2000 De Montfort University). Image processing III: Mathematical methods, algorithms and applications. Chichester: Horwood Pub. for the Institute of Mathematics and its Applications, 2001.

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27

Pinoli, Jean-Charles. Mathematical Foundations of Image Processing and Analysis 1. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118625705.

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28

Serra, Jean, and Pierre Soille, eds. Mathematical Morphology and Its Applications to Image Processing. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1040-2.

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29

Pinoli, Jean-Charles. Mathematical Foundations of Image Processing and Analysis 2. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118984574.

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30

Tai, Xue-Cheng, Suhua Wei, and Haiguang Liu, eds. Mathematical Methods in Image Processing and Inverse Problems. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2701-9.

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31

Image structure. Dordrecht: Kluwer Academic Publishers, 1997.

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32

1954-, Ronse Christian, Najman Laurent, and Decencière Etienne, eds. Mathematical morphology: 40 years on: Proceedings of the 7th International Symposium on Mathematical Morphology, April 18-20, 2005. Dordrecht: Springer, 2005.

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33

Mathematical statistics and data analysis. 2nd ed. Belmont, CA: Duxbury Press, 1995.

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34

Mathematical statistics and data analysis. 3rd ed. Belmont, CA: Thomson/Brooks/Cole, 2007.

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35

Mathematical statistics and data analysis. Monterey, Calif: Brooks/Cole Pub. Co., 1988.

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36

E, Christensen Gary, Fitzpatrick J. Michael, Rueckert Daniel, and SpringerLink (Online service), eds. Biomedical Image Registration: 5th International Workshop, WBIR 2012, Nashville, TN, USA, July 7-8, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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37

Dahlhaus, Rainer, Jürgen Kurths, Peter Maass, and Jens Timmer, eds. Mathematical Methods in Signal Processing and Digital Image Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75632-3.

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38

1966-, Hoeting Jennnifer A., ed. Computational statistics. Hoboken, N.J: Wiley-Interscience, 2005.

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39

service), SpringerLink (Online, ed. Computational statistics. Dordrecht: Springer, 2009.

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40

Givens, Geof H. Computational statistics. 2nd ed. Hoboken, N.J: Wiley, 2013.

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41

Astola, Jaakko, and Edward R. Dougherty. Mathematical nonlinear image processing: A special issue of the Journal of mathematical imaging and vision. New York: Springer Science+Business Media, LLC, 1993.

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42

Shing-Chow, Chan, and Kang Sing Bing, eds. Image-based rendering. New York: Springer, 2007.

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43

R, Dougherty Edward, ed. Mathematical morphology in image processing. New York: M. Dekker, 1993.

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44

Maccone, Claudio. Mathematical SETI: Statistics, Signal Processing, Space Missions. Springer, 2017.

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45

Zoubir, Abdelhak M., and D. Robert Iskander. Bootstrap Techniques for Signal Processing. Cambridge University Press, 2004.

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46

Connie, Page, LePage Raoul 1938-, and Symposium on the Interface: Computing Science and Statistics (22nd : 1990 : Kellogg Center), eds. Computing science and statistics: Statistics of many parameters : curves, images, spatial models. New York: Springer-Verlag, 1992.

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47

(Editor), Joachim Weickert, and Hans Hagen (Editor), eds. Visualization and Processing of Tensor Fields (Mathematics and Visualization). Springer, 2005.

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48

Françoise, Prêteux, Mohammad-Djafari Ali, Dougherty Edward R, Society of Photo-optical Instrumentation Engineers., and Society for Industrial and Applied Mathematics., eds. Mathematical modeling, Bayesian estimation, and inverse problems: 21-23 July 1999, Denver, Colorado. Bellingham, Wash., USA: SPIE, 1999.

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49

Réfrégier, Phillipe. Statistical Image Processing Techniques for Noisy Images. Springer, 2013.

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50

Fermüller, Cornelia. Motion Illusions in Man and Machine. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0006.

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Abstract:
At the level of mathematical abstraction, computing image motion amounts to an estimation problem and can be analyzed using the tools of statistics and signal processing. As shown in this chapter, intrinsic limitations to the estimation processes make it impossible to derive veridical estimates for all images. Image motion is estimated erroneously, and as a result higher level processes compute erroneous three-dimensional motion and moving scenes. Specifically, two limitations are discussed: (a) due to noise in image data, there is statistical bias that affects anisotropic patterns and (2) the filters computing changes in time are asymmetric (causal), using data only from the past but not the future, and misestimate on locally asymmetric intensity signals of certain spatial frequencies. Since these limitations are not an artifact of the hardware but are inherent to the computations, they will affect any system and thus create illusions in man and machine.
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