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:
Introduction Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

12. Fuzzy-Based System for Corner Detection

verfasst von : Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas

Erschienen in: Engineering Applications of Soft Computing

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Excerpt

Corner detection is an important task in computer vision problems due to the complexity of determinate the shape of different regions within an image. Real-life image data are always inexact due to inherent uncertainties that may arise from the imaging capture process such as defocusing, illumination changes, noise, etc. Therefore, the localization and detection of corners has become a difficult task under research, in order to accomplish the detection process under such imperfect situations. On the other hand, Fuzzy systems are well known for their efficient handling capacities when they face uncertainness and incompleteness. Fuzzy systems use modelling concepts in the same way as a human do. Under this circumstances, corners could be modelled by means of linguistic rules. This chapter presents a corner detection algorithm which employs fuzzy reasoning. The robustness of the presented algorithm is compared to well-known conventional corner detectors and its performance is also tested over a number of benchmark images to illustrate the efficiency of the algorithm under uncertainty. …

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 Learning Automata Applied to Planning Control
Literatur
1.
Lowe DG (1985) Perceptual organization and visual recognition. Kluwer Academic Publishers, USACrossRef
2.
Loupias E, Sebe E (2000) Wavelet-based salient points: applications to image retrieval using color and texture features, in advances in visual information systems. In: Proceedings of the 4th international conference, VISUAL 2000, pp 223–232
3.
Fischler M, Wolf HC (1994) Locating perceptually salient points on planar curves. IEEE Trans Pattern Anal Mach Intell 16(2):113–129CrossRef
4.
Freeman H, Davis LS (1977) A corner-finding algorithm for chain-coded curves. IEEE Trans Comput C-26:297–303CrossRef
5.
Kitchen L, Rosenfeld A (1982) Gray-level corner detection. Pattern Recogn Lett 1:95–102CrossRef
6.
Zheng Z, Wang H, Teoh E (1999) Analysis of gray level corner detection. Pattern Recogn Lett 20(2):149–162CrossRefMATH
7.
Rattarangsi A, Chin RT (1992) Scale-based detection of corners of planar curves. IEEE Trans Pattern Anal Mach Intell 14(4):430–449CrossRef
8.
Teh C, Chin RT (1989) On the detection of dominant points on digital curves. IEEE Trans Pattern Anal Mach Intell 11(8):859–872CrossRef
9.
Rosenfeld A, Johnston E (1973) Angle detection on digital curves. IEEE Trans Comput C-22:858–875
10.
Harris C, Stephens M (1988) A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp 147–151
11.
Smith S, Brady M (1997) A new approach to low level image processing. Int J Comput Vision 23(1):45–78CrossRef
12.
Zou L, Chen J, Zhang J, Dou L (2008) The Comparison of Two Typical Corner Detection Algorithms. Second international symposium on intelligent information technology application (ISBN 978-0-7695-3497)
13.
14.
Pal SK, Ghosh A, Kundu MK (2000) Soft computing for image processing. Physica-Verlag, Philadelphia, pp 44–78 (Chapter 1)CrossRefMATH
15.
Yua D, Hu Q, Wua C (2007) Uncertainty measures for fuzzy relations and their applications. Appl Soft Comput 7(3):1135–1143CrossRef
16.
Karmakar G, Dooley L (2002) A generic fuzzy rule based in image segmentation algorithm. Pattern Recognit Lett 23:1215–1227CrossRefMATH
17.
18.
Jacquey F, Comby F, Strauss O (2008) Fuzzy Edge detection for omnidirectional images. Fuzzy Sets Syst 159:1991–2010MathSciNetCrossRef
19.
20.
Tizhoosh H (2003) Fast and Robust Fuzzy Edge Detection. In: Nachtegael et al (eds) Fuzzy filters for image processing. Springer, Berlin
21.
Liang L, Looney C (2003) Competitive Edge detection. Appl Soft Comput 3:123–137CrossRef
22.
Kim D, Lee W, Kweon I (2004) Automatic edge detection using 3 × 3 ideal binary pixel patterns and fuzzy-based edge thresholding. Pattern Recogn Lett 25:101–106CrossRef
23.
Russo F (2004) Impulse noise cancellation in image data using a two-output nonlinear filter. Measurement 36(3–4):205–213CrossRef
24.
Yüksel M (2007) Edge detection in noisy images by fuzzy processing. Int J Electron Commun 61:82–89CrossRef
25.
Banerjee M, Kundu MK (2008) Handling of impreciseness in gray level corner detection using fuzzy set theoretic approach. Appl Soft Comput 8(4):1680–1691CrossRef
26.
Várkonyi-Kóczy A (2008) Fuzzy logic supported corner detection. J Intell Fuzzy Syst 19:41–50MATH
27.
Russo F (1999) FIRE operators for image processing. Fuzzy Sets Syst, vol 103, pp 265–275
28.
Moravec H (1997) Towards automatic visual obstacle avoidance. In: Proceedings of the 5th international joint conference on artificial intelligence, p 584
29.
Schmid Cordelia, Mohr Rrger, Bauckhage Christian (2000) Evaluation of interest point detectors. Int J Comput Vision 37(2):151–172CrossRefMATH
30.
Mokhtarian F, Mohanna F (2006) Performance evaluation of corner detectors using consistency and accuracy measures. Comput Vis Image Underst 102(1):81–94CrossRef
Metadaten
Titel
Fuzzy-Based System for Corner Detection
verfasst von
Margarita-Arimatea Díaz-Cortés
Erik Cuevas
Raúl Rojas
Copyright-Jahr
2017
Buch
Engineering Applications of Soft Computing

Print ISBN: 978-3-319-57812-5

Electronic ISBN: 978-3-319-57813-2

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-57813-2_12