Books on the topic 'Classifications des images'

Xu, Xiang, Xingkun Wu, and Feng Lin. Cellular Image Classification. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47629-2.

M, Benning Vivien, and Ching Neville P, eds. Classification of remotely sensed images. Bristol: A. Hilger, 1987.

Ahmed, Nazeer. Image shape classification techniques. Manchester: University of Manchester, 1997.

Geimer, Robert L. Flake classification by image analysis. Madison, WI: U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, 1988.

Bi, Ying, Bing Xue, and Mengjie Zhang. Genetic Programming for Image Classification. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65927-1.

Jenicka, S. Land Cover Classification of Remotely Sensed Images. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66595-1.

Yin, Xiao-Xia, Sillas Hadjiloucas, and Yanchun Zhang. Pattern Classification of Medical Images: Computer Aided Diagnosis. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57027-3.

Kamusoko, Courage. Remote Sensing Image Classification in R. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8012-9.

Borra, Surekha, Rohit Thanki, and Nilanjan Dey. Satellite Image Analysis: Clustering and Classification. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6424-2.

Costa, Luciano da Fontoura. Shape classification and analysis: Theory and practice. 2nd ed. Boca Raton: Taylor & Francis, 2009.

Tang, Xiaoou. Dominant run-length method for image classification. [Woods Hole, Mass: Woods Hole Oceanographic Institution, 1997.

Tang, Xiaoou. Dominant run-length method for image classification. [Woods Hole, Mass: Woods Hole Oceanographic Institution, 1997.

International Conference on GeoInformatics (16th 2008 Guangzhou, China). Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of remote sensing images : 28-29 June 2008, Guangzhou, China. Edited by Liu Lin 1965-, Zhongshan da xue (Guangzhou, China), University of Cincinnati, Association of Chinese Professionals in Geographic Information Systems (Abroad), Guangzhou di li yan jiu suo, Guo jia zi ran ke xue ji jin wei yuan hui (China), SPIE (Society), and Joint Conference on GIS and Built environment (2008 : Guangzhou, China). Bellingham, Wash: SPIE, 2008.

Tao, Linmi, and Atif Mughees. Deep Learning for Hyperspectral Image Analysis and Classification. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4420-4.

Hyperspectral imaging: Techniques for spectral detection and classification. New York: Kluwer Academic/Plenum Publishers, 2003.

Zharkova, Valentina, and Lakhmi C. Jain, eds. Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-47518-7.

Canty, Morton John. Image Analysis, Classification, and Change Detection in Remote Sensing. Fourth edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429464348.

Linares-Herrera, Pedro. Texture analysis and classification on the characterization of ultrasonic images of the placenta. [S.l: The Author], 2004.

Lemeshewsky, George. Land cover classification from SPOT multispectral and panchromatic images using neural network classification of fuzzy clustered spectral and textural features. [Reston, VA]: U.S. Geological Survey, 1995.

1941-, Hart Peter E., and Stork David G, eds. Pattern classification. 2nd ed. New York: Wiley, 2001.

Young, Lois E. The royal image: A classification of British royal portraits on stamps. Hampstead Norreys, Newbury, Berkshire, England: Cockrill, 1987.

Healey, Anthony J. Sonar signal acquisition and processing for identification and classification of ship hull fouling. Monterey, Calif: Naval Postgraduate School, 1993.

Perry, David Robert. Target detection and scene classification with VNIR/SWIR spectral imagery. Monterey, Calif: Naval Postgraduate School, 2000.

Rutkowski, Leszek. New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

Rutkowski, Leszek. New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-40046-2.

Saaremets, Aili. The influence of television on body classification: Does personality affect people's vulnerability to passive images? Sudbury, Ont: Laurentian University, Department of Psychology, 2000.

Sithole, George. Segmentation and classification of airborne laser scanner data. Delft: Nederlandse Commissie voor Geodesie, 2005.

Dougherty, Geoff. Pattern Recognition and Classification: An Introduction. New York, NY: Springer New York, 2013.

Sanders, Jeffrey D. Target detection and classification at Kernel Blitz 1997 using spectral imagery. Monterey, Calif: Naval Postgraduate School, 1999.

service), SpringerLink (Online, ed. Soft Computing Approach to Pattern Classification and Object Recognition: A Unified Concept. New York, NY: Springer New York, 2012.

Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.

Canty, Morton John. Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.

Feng, Lin, Xiang Xu, and Xingkun Wu. Cellular Image Classification. Springer, 2016.

Feng, Lin, Xiang Xu, and Xingkun Wu. Cellular Image Classification. Springer, 2018.

Image Recognition and Classification. New York: Marcel Dekker, Inc., 2003.

McGuire, Peter Frederick. Image classification using eigenpaxels. 1999.

Biomedical Image Processing and Classification. MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-0347-9.

Wilkerson, John D., Nora Webb Williams, and Andreu Casas. Images As Data for Social Science Research: An Introduction to Convolutional Neural Nets for Image Classification. Cambridge University Press, 2020.

A, Schowengerdt Robert, and Research Institute for Advanced Computer Science (U.S.), eds. The effect of lossy image compression on image classification. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1995.

The effect of lossy image compression on image classification. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1995.

Zhang, Yanchun, Sillas Hadjiloucas, and Xiao-Xia Yin. Pattern Classification of Medical Images: Computer Aided Diagnosis. Springer, 2018.

Emotion Classification in Images Using Amazon's Rekognition API. United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529604412.

Kamusoko, Courage. Remote Sensing Image Classification in R. Springer, 2019.

Dey, Nilanjan, Rohit Thanki, and Surekha Borra. Satellite Image Analysis: Clustering and Classification. Springer, 2019.

Gnagy, Caroline. Goin’ Hillbilly Nuts. Edited by Travis D. Stimeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190248178.013.13.

Nielsen, Tore. Microdream Neurophenomenology. Edited by Kalina Christoff and Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.11.

Javidi, Bahram. Image Recognition and Classification (Optical Engineering, 78). CRC, 2002.

Comparisons of neural networks to standard techniques for image classification and correlation. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1994.

Bahram, Javidi, ed. Image recognition and classification: Algorithms, systems, and applications. New York: Marcel Dekker, 2002.

Thenkabail, Prasad S., John G. Lyon, and Alfredo Huete, eds. Hyperspectral Indices and Image Classifications for Agriculture and Vegetation. CRC Press, 2018. http://dx.doi.org/10.1201/9781315159331.