Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Theoretical and Practical Solutions in Remote Sensing Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

3. Vision-Based Change Detection Using Comparative Morphology

verfasst von : Yu. Vizilter, A. Rubis, O. Vygolov, S. Zheltov

Erschienen in: Computer Vision in Control Systems-3

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The chapter addresses the theoretical and practical aspects of the scene change detection problem with the use of computer vision techniques. It means detecting new or disappeared objects in images registered at different moments of time and possibly in various lighting, weather, and season conditions. In this chapter, we propose the new scheme of Comparative Morphology (CM) as a generalization of the Morphological Image Analysis (MIA) scheme originally proposed by Pyt’ev. The CMs are the mathematical shape theories, which solve the tasks of the image similarity estimation, image matching, and change detection by means of some special morphological models and tools. The original morphological change detection approach is based on the analysis of difference between the test image and its projection to the shape of reference image. In our generalized approach, the morphological filter-projector is substituted by the comparative morphological filter with weaker properties, which transforms the test image guided by the local shape of reference image. Following theoretical aspects are addressed in this chapter: the comparative morphology, change detection scheme based on morphological comparative filtering, diffusion morphology, and morphological filters based on guided contrasting. Following practical aspects are addressed: the pipeline for change detection in remote sensing data based on comparative morphology and implementation of change detection scheme based on both guided contrasting and diffusion morphology. The chapter also contains the results of qualitative and quantitative experiments on a wide set of real images including the public benchmark.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Multidimensional Image Models and Processing
Nächstes Kapitel Methods of Filtering and Texture Segmentation of Multicomponent Images
Literatur
1.
Singh, A.: Review article digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989–1003 (1989)CrossRef
2.
Lu, D., Mausel, P., Brondízioc, E., Moran, E.: Change detection techniques. Int. J. Remote Sens. 25(12), 2365–2401 (2004)CrossRef
3.
Chen, J., Lu, M., Chen, X., Chen, J., Chen, L.: A spectral gradient difference based approach for land cover change detection. ISPRS J. Photogramm. Remote Sens. 85, 1–12 (2013)CrossRef
4.
Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D.: Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J. Photogramm. Remote Sens. 80, 91–106 (2013)CrossRef
5.
Pyt’ev, Y.P.: Morphological image analysis. Pattern Recognit. Image Anal. 3(1), 19–28 (1993)
6.
Vizilter, Y., Pyt’ev, Y., Chulichkov, A., Mestetskiy, L.: Morphological image analysis for computer vision applications. In: Favorskaya, M.N., Jain, L.C. (eds.) Computer Vision in Control Systems-1. Mathematical Theory. Intelligent Systems Reference Library. vol. 73, pp. 9–58. Springer International Publishing, Switzerland (2015)
7.
He, K., Sun, J., Tang, X.: Guided image filtering. In: Daniilidis, K., Maragos, P., Nikos Paragios, N. (eds.) Computer Vision—ECCV 2010, LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010)CrossRef
8.
Zhang, Q., Shen, X., Xu, L., Jia, J.: Rolling guidance filter. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision—ECCV 2014, LNCS, vol. 8691, pp. 815–830. Springer, Heidelberg (2014)
9.
Vizilter, Y.V., Gorbatsevich, V.S., Rubis, A.Y., Zheltov, S.Y.: Shape-based image matching using heat kernels and diffusion maps. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XL(3), 357–364 (2014)
10.
Vizilter, Y.V., Rubis, A.Y., Zheltov, S.Y., Vygolov, O.V.: Change detection via morphological comparative filters. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. III(3), 279–286 (2016)
11.
Quarmby, N.A., Cushnie, J.L.: Monitoring urban land cover changes at the urban fringe from SPOT HRV imagery in south-east England. Int. J. Remote Sens. 10(6), 953–963 (1989)CrossRef
12.
Coppin, P.R., Bauer, M.E.: Digital change detection in forest ecosystems with remote sensing imagery. Remote Sens. Rev. 13, 207–234 (1996)CrossRef
13.
Lu, D., Mausel, P., Batistella, M., Moran, E.: Land-cover binary change detection methods for use in the moist tropical region of the Amazon: a comparative study. Int. J. Remote Sens. 26(1), 101–114 (2005)CrossRef
14.
Gao, J.: Digital Analysis of Remotely Sensed Imagery. McGraw-Hill, New York (2009)
15.
Howarth, P.J., Wickware, G.M.: Procedures for change detection using Landsat digital data. Int. J. Remote Sens. 2(3), 277–291 (1981)CrossRef
16.
Ludeke, A.K., Maggio, R.C., Reid, L.M.: An analysis of anthropogenic deforestation using logistic regression and GIS. J. Environ. Manag. 31(3), 247–259 (1990)CrossRef
17.
Lunetta, R.S.: Applications, project formulation, and analytical approach. In: Lunetta, R.S., Elvidge, C.D. (eds.) Remote Sensing Change Detection: Environmental Monitoring Methods and Applications, pp. 1–19. Taylor & Francis, London (1999)
18.
Chen, J., Gong, P., He, C., Pu, R., Shi, P.: Land-use/land-cover change detection using improved change-vector analysis. Photogramm. Eng. Remote Sens. 69(4), 369–379 (2003)CrossRef
19.
Nackaerts, K., Vaesen, K., Muys, B., Coppin, P.: Comparative performance of a modified change vector analysis in forest change detection. Int. J. Remote Sens. 26(5), 839–852 (2005)CrossRef
20.
Bayarjargal, Y., Karnieli, A., Bayasgalan, M., Khudulmur, S., Gandush, C., Tucker, C.J.: A comparative study of NOAA–AVHRR derived drought indices using change vector analysis. Remote Sens. Environ. 105(1), 9–22 (2006)CrossRef
21.
Richards, J.A.: Thematic mapping from multitemporal image data using the principal components transformation. Remote Sens. Environ. 16(1), 35–46 (1984)CrossRef
22.
Deng, J.S., Wang, K., Deng, Y.H., Qi, G.J.: PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data. Int. J. Remote Sens. 29(16), 4823–4838 (2008)CrossRef
23.
Kauth, R.J., Thomas, G.S.: The tasselled cap—a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. In: Symposium on Machine Processing of Remotely Sensed Data, vol. 4(B), pp. 41–51 (1976)
24.
Jin, S., Sader, S.A.: Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sens. Environ. 94(3), 364–372 (2005)CrossRef
25.
Rogan, J., Franklin, J., Roberts, D.A.: A comparison of methods for monitoring multitemporal vegetation change using thematic mapper imagery. Remote Sens. Environ. 80(1), 143–156 (2002)CrossRef
26.
Tomowski, D., Ehlers, M., Klonus, S.: Colour and texture based change detection for urban disaster analysis. In: Joint Urban Remote Sensing Event (JURSE’2011), pp. 329–332 (2011)
27.
Erbek, F.S., Özkan, C., Taberner, M.: Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. Int. J. Remote Sens. 25(9), 1733–1748 (2004)CrossRef
28.
Bouziani, M., Goïta, K., He, D.-C.: Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge. ISPRS J. Photogramm. Remote Sens. 6591, 143–153 (2010)
29.
Im, J., Jensen, J.R.: A change detection model based on neighborhood correlation image analysis and decision tree classification. Remote Sens. Environ. 99(3), 326–340 (2005)CrossRef
30.
Jensen, J.R.: Introductory Digital Image Processing: A Remote Sensing Perspective. Prentice Hall, Toronto (2005)
31.
Miller, A.B., Bryant, E.S., Birnie, R.W.: An analysis of land cover changes in the Northern Forest of New England using multitemporal Landsat MSS data. Int. J. Remote Sens. 19(2), 245–265 (1998)CrossRef
32.
Yuan, F., Sawaya, K.E., Loeffelholz, B.C., Bauer, M.E.: Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal landsat remote sensing. Remote Sens. Environ. 98(2–3), 317–328 (2005)CrossRef
33.
Ji, W., Ma, J., Twibell, R.W., Underhill, K.: Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics. Comput. Environ. Urban Syst. 30(6), 861–879 (2006)CrossRef
34.
Ghosh, A., Mishra, N.S., Ghosh, S.: Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Inf. Sci. 181(4), 699–715 (2011)CrossRef
35.
Chan, J.C.-W., Chan, K.-P., Yeh, A.G.-O.: Detecting the nature of change in an urban environment: a comparison of machine learning algorithms. Photogramm. Eng. Remote Sens. 67(2), 213–225 (2001)
36.
Dai, X.L., Khorram, S.: Remotely sensed change detection based on artificial neural networks. Photogramm. Eng. Remote Sens. 65(10), 1187–1194 (1999)
37.
Lillesand, T.M., Kiefer, R.W., Chipman, J.W.: Remote Sensing and Image Interpretation, 6th edn. Wiley, Hoboken, NJ (2008)
38.
Lunetta, R.S., Knight, J.F., Ediriwickrema, J., Lyon, J.G., Worthy, L.D.: Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sens. Environ. 105(2), 142–154 (2006)CrossRef
39.
Mas, J.F.: Monitoring land-cover changes: a comparison of change detection techniques. Int. J. Remote Sens. 20(1), 139–152 (1999)CrossRefMathSciNet
40.
Collins, J.B., Woodcock, C.E.: An assessment of several linear change detection techniques for mapping forest mortality using multitemporal landsat TM data. Remote Sens. Environ. 56(1), 66–77 (1996)CrossRef
41.
Schowengerdt, R.A.: Techniques for Image Processing and Classification in Remote Sensing. Academic Press, New York (1983)
42.
Gopal, S., Woodcock, C.: Remote sensing of forest change using artificial neural networks. IEEE Trans. Geosci. Remote Sens. 34(2), 398–404 (1996)CrossRef
43.
Abuelgasim, A.A., Ross, W.D., Gopal, S., Woodcock, C.E.: Change detection using adaptive fuzzy neural networks: environmental damage assessment after the Gulf war. Remote Sens. Environ. 70(2), 208–223 (1999)CrossRef
44.
Woodcock, C.E., Macomber, S.A., Pax-Lenney, M., Cohen, W.B.: Monitoring large areas for forest change using Landsat: generalization across space, time and Landsat sensors. Remote Sens. Environ. 78(1–2), 194–203 (2001)CrossRef
45.
Liu, X., Lathrop, R.G.: Urban change detection based on an artificial neural network. Int. J. Remote Sens. 23(12), 2513–2518 (2002)CrossRef
46.
Pijanowski, B.C., Pithadia, S., Shellito, B.A., Alexandridis, K.: Calibrating a neural network-based urban change model for two metropolitan areas of the Upper Midwest of the United States. Int. J. Geograph. Inf. Sci. 19(2), 197–215 (2005)CrossRef
47.
Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (2000)CrossRefMATH
48.
Huang, C., Song, K., Kim, S., Townshend, J.R.G., Davis, P., Masek, J.G., Goward, S.N.: Use of a dark object concept and support vector machines to automate forest cover change analysis. Remote Sens. Environ. 112(3), 970–985 (2008)CrossRef
49.
Bovolo, F., Bruzzone, L., Marconcini, M.: A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure. IEEE Trans. Geosci. Remote Sens. 46(7), 2070–2082 (2008)CrossRef
50.
Nemmour, H., Chibani, Y.: Multiple support vector machines for land cover change detection: an application for mapping urban extensions. ISPRS J. Photogramm. Remote Sens. 61(2), 125–133 (2006)CrossRef
51.
Makkeasorn, A., Chang, N.-B., Li, J.: Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed. J. Environ. Manag. 90(2), 1069–1080 (2009)CrossRef
52.
Pa, M.: Random forest classifier for remote sensing classification. Int. J. Remote Sens. 26(1), 217–222 (2005)CrossRef
53.
Sesnie, S.E., Gessler, P.E., Finegan, B., Thessler, S.: Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments. Remote Sens. Environ. 112(5), 2145–2159 (2008)CrossRef
54.
Smith, G.M.: The development of integrated object-based analysis of EO data within UK national land cover products object-based image analysis. In: Blaschke, T., Lang, S., Hay, G.J. (eds.) Object-Based Image Analysis, LNGC, pp. 513–528. Springer, Berlin, Heidelberg (2008)CrossRef
55.
Yang, Q., Li, X., Shi, X.: Cellular automata for simulating land use changes based on support vector machines. Comput. Geosci. 34(6), 592–602 (2008)CrossRef
56.
Lefebvre, A., Corpetti, T., Hubert-Moy, L.: Object-oriented approach and texture analysis for change detection in very high resolution images. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS’2008), pp. IV.663–IV.666 (2008)
57.
Zhou, W., Troy, A., Grove, M.: Object-based land cover classification and change analysis in the baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors 8(3), 1613–1636 (2008)CrossRef
58.
Miller, O., Pikaz, A., Averbuch, A.: Objects based change detection in a pair of gray-level images. Pattern Recognit. 38(11), 1976–1992 (2005)CrossRef
59.
Hall, O., Hay, G.J.: A multiscale object-specific approach to digital change detection. Int. J. Appl. Earth Observ. Geoinf. 4(4), 311–327 (2003)CrossRef
60.
Chant, T.D., Kelly, M.: Individual object change detection for monitoring the impact of a forest pathogen on a hard wood forest. Photogramm. Eng. Remote Sens. 75(8), 1005–1013 (2009)CrossRef
61.
Hazel, G.G.: Object-level change detection in spectral imagery. IEEE Trans. Geosci. Remote Sens. 39(3), 553–561 (2001)CrossRef
62.
Li, J., Narayanan, R.M.: A shape-based approach to change detection of lakes using time series remote sensing images. IEEE Trans. Geosci. Remote Sens. 41(11), 2466–2477 (2003)CrossRef
63.
Blaschke, T.: Towards a framework for change detection based on image objects. Göttinger Geographische Abhandlungen 113, 1–9 (2005)
64.
Durieux, L., Lagabrielle, E., Nelson, A.: A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data. ISPRS J. Photogramm. Remote Sens. 63(4), 399–408 (2008)CrossRef
65.
Hansen, M.C., Loveland, T.R.: A review of large area monitoring of land cover change using Landsat data. Remote Sens. Environ. 122, 66–74 (2012)CrossRef
66.
Holland, D.A., Sanchez-Hernandez, C., Gladstone, C.: Detecting changes to topographic features using high resolution imagery. In: XXXVII ISPRS Congress Proceedings, pp. 1153–1158 (2008)
67.
Xian, G., Homer, C.: Updating the 2001 national land cover database impervious surface products to 2006 using Landsat imagery change detection methods. Remote Sens. Environ. 114(8), 1676–1686 (2010)CrossRef
68.
Xian, G., Homer, C., Fry, J.: Updating the 2001 national land cover database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sens. Environ. 113(6), 1133–1147 (2009)CrossRef
69.
Desclée, B., Bogaert, P., Defourny, P.: Forest change detection by statistical object-based method. Remote Sens. Environ. 102(1–2), 1–11 (2006)CrossRef
70.
Gamanya, R., De Maeyer, P., De Dapper, M.: Object-oriented change detection for the city of Harare, Zimbabwe. Expert Syst. Appl. 36(1), 571–588 (2009)CrossRef
71.
Bontemps, S., Bogaert, P., Titeux, N., Defourny, P.: An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution. Remote Sens. Environ. 112(6), 3181–3191 (2008)CrossRef
72.
Conchedda, G., Durieux, L., Mayaux, P.: An object-based method for mapping and change analysis in mangrove ecosystems. ISPRS J. Photogramm. Remote Sens. 63(5), 578–589 (2008)CrossRef
73.
Stow, D., Hamada, Y., Coulter, L., Anguelova, Z.: Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery. Remote Sens. Environ. 112(3), 1051–1061 (2008)CrossRef
74.
Duveiller, G., Defourny, P., Desclée, B., Mayaux, P.: Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts. Remote Sens. Environ. 112(5), 1969–1981 (2008)CrossRef
75.
Al-Khudhairy, D.H.A., Caravaggi, I., Giad, S.: Structural damage assessments from Ikonos data using change detection, object-oriented segmentation, and classification techniques. Photogramm. Eng. Remote Sens. 71(5), 825–837 (2005)CrossRef
76.
Niemeyer, I., Nussbaum, S.: Change detection: The potential for nuclear safeguards verifying treaty compliance. In: Avenhaus, R., Kyriakopoulos, N., Richard, M., Stein, G. (eds.) Verifying Treaty Compliance, pp. 335–348. Springer, Berlin Heidelberg (2006)CrossRef
77.
McDermid, G.J., Linke, J., Pape, A.D., Laskin, D.N., McLane, A.J., Franklin, S.E.: Object-based approaches to change analysis and thematic map update: challenges and limitations. Can. J. Remote Sens. 34(5), 462–466 (2008)CrossRef
78.
Niemeyer, I., Marpu, P.R., Nussbaum, S.: Change detection using object features. In: Blaschke, T., Lang, S., Hay, G.J. (eds.) Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, LNGC, pp. 185–201. Springer, Berlin, Heidelberg (2008)CrossRef
79.
Pyt’ev, Y.P.: The morphology of color (multispectral) images. Pattern Recognit. Image Anal. 7(4), 467–473 (1997)
80.
Pyt’ev, Y.P., Falomkin, I.I., Chulichkov, A.I.: Morphological compression of grayscale images of text. Pattern Recognit. Image Anal. 16(3), 523–528 (2006)
81.
Evsegneev, S.O., Pyt’ev, Y.P.: Analysis and recognition of piecewise constant texture images. Pattern Recognit. Image Anal. 16(3), 398–405 (2006)
82.
Vizilter, Y.V., Zheltov, S.Y.: Geometrical correlation and matching of 2D image shapes. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. I-3, 191–196 (2012)
83.
Pyt’ev, Y.P.: Oblique projectors and relative forms in image morphology. J. Comput. Math. Math. Phys. 53(1), 1916–1937 (2013)
84.
Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, Inc., Orlando (1982)MATH
85.
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16(2), 187–198 (1997)CrossRef
86.
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRef
87.
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRef
88.
Scholkopf, B., Smola, A.J., Muller, K.-R.: Kernel principal component analysis. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods: Support Vector Learning, pp. 327–352. MIT Press (1999)
89.
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv. Neural. Inf. Process. Syst. 14, 585–591 (2001)
90.
Donoho, D., Grimes, C.: Hessian eigenmaps: locally linear embedding techniques for high dimensional data. Proc. Natl. Acad. Sci. 100(10), 5591–5596 (2003)CrossRefMATHMathSciNet
91.
Gashler, M., Ventura, D., Martinez, T.: Iterative non-linear dimensionality reduction by manifold sculpting. Adv. Neural Inf. Process. Syst. 20, 513–520 (2008)
92.
Lafon, S.: Diffusion Maps and Geometric Harmonics. Ph.D. thesis, Yale University, Department of Mathematics & Applied Mathematics (2004)
93.
94.
Coifman, R., Lafon, S., Maggioni, M., Keller, Y., Szlam, A., Warner, F., Zucker, S.: Geometries of sensor outputs, inference and information processing.  Proc. SPIE Intell. Integr. Microsyst. 6232 (2006). doi:10.​1117/​12.​669723
95.
Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Trans. Image Process. 16(2), 349–366 (2007)CrossRefMathSciNet
96.
97.
Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: Eurographics Symposium on Geometry Processing, vol. 28, no. 5, pp. 1383–1392 (2009)
98.
de Goes, F., Goldenstein, S., Velho, L.: A hierarchical segmentation of articulated bodies. Comput. Graph. Forum 27(5), 1349–1356 (2008)CrossRef
99.
100.
Reuter, M., Wolter, F.-E., Peinecke, N.: Laplace-Beltrami spectra as “Shape-DNA” of surfaces and solids. Comput. Aided Des. 38(4), 342–366 (2006)CrossRef
101.
Memoli, F.: A spectral notion of Gromov-Wasserstein distance and related methods. App. Comput. Harmon. Anal. 30(3), 363–401 (2011)CrossRefMATHMathSciNet
102.
Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) Computer Vision—ECCV 2004, Part 1, LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)CrossRef
103.
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)CrossRefMATH
Metadaten
Titel
Vision-Based Change Detection Using Comparative Morphology
verfasst von
Yu. Vizilter
A. Rubis
O. Vygolov
S. Zheltov
Copyright-Jahr
2018
Buch
Computer Vision in Control Systems-3

Print ISBN: 978-3-319-67515-2

Electronic ISBN: 978-3-319-67516-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-67516-9_3