Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
3D Shapes Isometric Deformation Using in-tSNE Read chapter Read first chapter

2015 | OriginalPaper | Chapter

A New Method of Object Saliency Detection in Foggy Images

Authors : Wenjun Lu, Xiaoning Sun, Congli Li

Published in: Image and Graphics

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Aiming to saliency detection problem of degraded foggy images, a new method of object saliency detection method in foggy images based on region covariance matrix is presented. In the method, color, direction and space information are extracted to form covariance feature description matrix according to characteristics of foggy images. Then local saliency sub-map is acquired by local contrast. In the same time, Wiener filter and Sobel edge detection are used to obtain global saliency sub-map. Finally, local saliency map of color domain is optimized by edge global saliency map, and saliency map is completed. Experiments show that compared with state-of-art methods, the proposed method has better adaptability and accuracy to object saliency detection in foggy images.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter A New Approach for Orthogonal Representation of Lunar Contour Maps
next chapter A New Optimization Algorithm for HEVC
Literature
1.
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurbiology 4, 219–227 (1985)
2.
Borji, A., Cheng, M.M., Jiang, H.Z., Li, J.: Salient object detection: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) (2014, accepted)
3.
He, K.M., Sun, J., Zhou, X.O.: Single image haze removal using dark channel prior. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), pp. 1956–1963. IEEE, Washington DC (2009)
4.
Jing, Y.U., DaPeng, L., QingMin, L.: Physics-based fast single image fog removal. ACTA AUTOMATICA SINICA 37(2), 143–149 (2011)CrossRef
5.
Fan, G., Zixing, C., Bin, X., et al.: New algorithm of automatic haze removal for single image. J. Image Grap. 16(4), 516–521 (2011)
6.
Li, C., Lu, W., Xue, S., Shi, Y.: Research on quality improvement of polarization imaging in foggy conditions. In: Sun, C., Fang, F., Zhou, Z.-H., Yang, W., Liu, Z.-Y. (eds.) IScIDE 2013. LNCS, vol. 8261, pp. 208–215. Springer, Heidelberg (2013)CrossRef
7.
Li, C., Lu, W., Xue, S., Shi, Y., Sun, X.: Quality assessment of polarization analysis images in foggy conditions. In: Proceedings of the IEEE International Conference on Image Processing(ICIP), pp. 551–555. IEEE, Pairs (2014)
8.
Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 589–600. Springer, Heidelberg (2006)CrossRef
9.
Föerstner, W., Moonen, B.: A metric for covariance matrices. Technical report, Department of Geodesy and Geoinformatics, Stuttgart University (1999)
10.
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 2012, 54–125 (1998)
11.
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Process Systems(NIPS), pp. 545–552. MIT Press, Massachusetts (2007)
12.
Achanta, R., Hemami, S., Estrada, F., Süsstrunk, S.: Frequency-tuned salient region detection. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition(CVPR), p. 1597. IEEE, Miami (2009)
13.
Zhang, L., Gu, Z.Y., Zhang, H.Y.: SDSP : a novel saliency detection method by combing simple priors. In: Proceedings of the IEEE International Conference on Image Processing(ICIP), pp. 171–175. Springer, Melbourne (2013)
14.
Margolin, R., Tal, A., Zelnik-Manor, L.: What makes a patch distinct. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition(CVPR), pp. 1139–1146. IEEE, Portland (2013)
Metadata
Title
A New Method of Object Saliency Detection in Foggy Images
Authors
Wenjun Lu
Xiaoning Sun
Congli Li
Copyright Year
2015
Book
Image and Graphics

Print ISBN: 978-3-319-21977-6

Electronic ISBN: 978-3-319-21978-3

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-21978-3_19

Premium Partner

    Image Credits