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:
Performance Analysis of Commodity Server with Freeware Remote Terminal Application in Homogeneous and Heterogeneous Mutli-computing Environments Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

A Systematic Review on Various Techniques on Image Segmentation

verfasst von : Diksha Thakur, Nitin Mittal, Saumya Srivastava

Erschienen in: Recent Innovations in Computing

Verlag: Springer Singapore

Einloggen

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

search-config
loading …

Abstract

In the present situation, image processing is one of the huge developing fields. It is a strategy which is ordinarily used to improve raw image which is collected from different assets. It is a sort of sign processing. The image segmentation is considered one of the most essential processes in the production of images. Some of these methods use only the histogram of the gray scale, some use spatial information, while others use conceptual approaches of the fuzzy set. In noisy environments, these methods are not appropriate. Some work has been done using the method of MRF, which is robust to noise but involves computing. This paper gives an outline of image processing techniques. The fundamental concern of this paper is to characterize different methods utilized in various periods of image processing.

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 An Algorithm to Recognize and Classify Circular Objects from Image on Basis of Their Radius
Nächstes Kapitel On Performance Analysis of Biometric Methods for Secure Human Recognition
Literatur
1.
Pratt, W.K.: Digital Image Processing. Wiley, New York (1978)MATH
2.
Pavlidis, T.: Structural Pattern Recognition. Springer, Heidelberg/New York (1977)CrossRef
3.
Rosenfeld, A., Kak, A.C.: Digifal Picture Processing, 2nd edn. Academic Press, New York/London (1982). (Vol. 2)MATH
4.
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Publishing House of Electronics Industry, Beijing (2007)
5.
Hertz, L., Schafer, R.W.: Multilevel thresholding using edge matching. Comput. Vis. Graph. Image Process. 44, 279–295 (1988)CrossRef
6.
Kohler, R.: A segmentation system based on thresholding. Comput. Graphics Image Process. 15, 319–338 (1981)CrossRef
7.
Wang, S., Haralick, R.M.: Automatic multithreshold selection. Comput. Vis. Graph. Image Process. 25, 46–67 (1984)CrossRef
8.
Baukharouba, S., Rebordao, J.M., Wendel, P.L.: An amplitude segmentation method based on the distribution function of an image. Comput. Vis. Graph. Image Process. 29, 47–59 (1985)CrossRef
9.
Carlotto, M.J.: Histogram analysis using scale-space approach. IEEE Trans. Pattern Anal. Machine Intell. PAMI-9, 121–129 (1987)
10.
Kapur, J.N., Sahoo, P.K., Wong, A.K.: A new method for grey level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29, 273–285 (1985)CrossRef
11.
Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognit. 19, 41–47 (1986)CrossRef
12.
Kang, W. X., Yang, Q. Q., Liang, R. R.: The comparative research on image segmentation algorithms. In: IEEE Conference on ETCS, pp. 703–707 (2009)
13.
ShiandJ, N., Pan, J.: An improved active contours model for image segmentation by level set method. Optik 127(3), 1037–1042 (2016)CrossRef
14.
Oliva, D., Cuevas, E., Pajares, G., Zaldivar, D., Osuna, V.: A multilevel thresholding algorithm using electromagnetism optimization. Neuro Comput. 139, 357–381 (2014)
15.
Akay, B.: A study on particles warm optimization and artificial bee colony algorithms for multilevel thresholding. Appl. Soft Comput. J. 13(6), 3066–3091 (2013)
16.
Osuna-Enciso, V., Cuevas, E., Sossa, H.: A comparison of nature inspired algorithms for multi-threshold image segmentation. Expert Syst. Appl. 40(4), 1213–1219 (2013)
17.
Kurban, T., Civicioglu, P., Kurban, R., Besdok, E.: (2014). Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Appl. Soft Comput. J. 23, 128–143 (2014)
18.
Sarkar, S., Das, S., Chaudhuri, S.S.: A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution. Pattern Recogn. Lett. 54, 27–35 (2015)CrossRef
19.
20.
Mahmud, S.A., Mohammed, J.B., Hasan, M.S., Alzghool, M.: A survey of digital image processing techniques in character recognition. IJCSNS Int. J. Comput. Sci. Netw. Security 14(3) (2014)
21.
Rao, K.M.: Overview of image processing. Reading Images (2006)
22.
Kaur, A.: A review paper on image segmentation and its various techniques in image processing. Int. J. Sci. Res. 3(12) (2014)
23.
Aly, A.A., Deris, S.B., Zaki, N.: Research review for digital image segmentation techniques. Int. J. Comput. Sci. Inf. Technol. 3(5) (2011)
24.
Chiranjeevi, K., Jena, U.: Fast vector quantization using a Bat algorithm for image compression. Eng. Sci. Technol. Int. J. 19(2), 769–781 (2016)
25.
Karri, C., Jena, U.: Image compression based on vector quantization using cuckoo search optimization technique. Ain Shams Eng. J. (2016) (in press)
26.
Baradez, M.O., McGuckin, C.P., Forraz, N., Pettengell, R., Hoppe: A robust and automated unimodal histogram thresholding and potential applications. Pattern Recogn. 37(6), 1131–1148 (2004)
27.
Yogamangalam, R.: Segmentation techniques comparison in image processing. Int. J. Eng. Technol. (IJET) 5(1) (2013)
28.
Kaganami, H., Beiji, Z.: Region based segmentation versus edge detection. Intell. Inf. Hiding Multimedia Signal Process. 1217–1221 (2009)
29.
30.
Li, D., Zhang, G., Wu, Z., Yi, L.: An edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation. IEEE Trans. 19, 2781–2787 (2010)
31.
Shih, F.Y., Cheng, S.: Adaptive mathematical morphology for edge linking. Inf. Sci. 167(4), 9–21 (2004)
32.
Comaniciu, D., Meer, P., Robust analysis of feature spaces color image segmentation. In: Proceedings of the IEEE CVPR Conference, pp. 750–755 (1997)
33.
34.
Dhawan, S.: A review of image compression and comparison of its algorithms. Int. J. Electronic. Commun. Technol. 2(1), (2011)
35.
Wallace, G.K.: The JPEG still picture compression. Standard Comm ACM 34(4) (1991)
36.
37.
Li, P., Li, H.O.: Fuzzy techniques in image restoration research—a survey. Int. J. Comput. Cogn. 2(2), 131–149 (2004)
38.
Maru, M.: Image restoration techniques: a survey. Int. J. Comput. Trends Technol. 3(12) (2014)
39.
Moustakides, G., Briassoulis, D., Psarakis, E., Dimas, E.: 3D image acquisition and NURBS based geometry modelling of natural objects. In: Advances in Engineering Software, pp. 955–969 (2000)
40.
Kuriakose, B., Preena, K.P.: A Review on 2D image representation methods. Int. J. Eng. Res. Technol. (IJERT) 4(4) (2015)
41.
Kaur, G.: Image enhancement and its techniques, a review. Int. J. Computer Trends Technol. (IJCTT) 3(12) (2014)
Metadaten
Titel
A Systematic Review on Various Techniques on Image Segmentation
verfasst von
Diksha Thakur
Nitin Mittal
Saumya Srivastava
Copyright-Jahr
2021
Verlag
Springer Singapore
Buch
Recent Innovations in Computing

Print ISBN: 978-981-15-8296-7

Electronic ISBN: 978-981-15-8297-4

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-981-15-8297-4_34