Advertisement

A Context Sensitive Thresholding Technique for Automatic Image Segmentation

  • Anshu Singla
  • Swarnajyoti Patra
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)

Abstract

Recently, energy curve of an image is defined for image analysis. The energy curve has similar characteristics as that of histogram but also incorporates the spatial contextual information of the image. In this work we proposed a thresholding technique based on energy curve of the image to find out the optimum number of thresholds for image segmentation. The proposed method applies concavity analysis technique existing in the literature on the energy curve to detect all the potential thresholds. Then a threshold elimination technique based on cluster validity measure is proposed to find out the optimum number of thresholds. To assess the effectiveness of proposed method the results obtained using energy curve of the image are compared with those obtained using histogram of the image. Experimental results on four different images confirmed the effectiveness of the proposed technique.

Keywords

Concavity analysis DB index Energy curve Histogram Segmentation 

References

  1. 1.
    Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)CrossRefGoogle Scholar
  2. 2.
    Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29, 273–285 (1985)CrossRefGoogle Scholar
  3. 3.
    Patra, S., Ghosh, S., Ghosh, A.: Histogram thresholding for unsupervised change detection of remote sensing images. Int. J. Remote Sens. 32, 6071–6089 (2011)CrossRefGoogle Scholar
  4. 4.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13, 146–165 (2004)CrossRefGoogle Scholar
  5. 5.
    Huang, L.K., Wang, M.J.J.: Image thresholding by minimizing the measures of fuzziness. Pattern Recogn. 28, 41–51 (1995)CrossRefGoogle Scholar
  6. 6.
    Huang, L.K., Wang, M.J.J.: Thresholding technique with adaptive window selection for uneven lighting image. Pattern Recogn. Lett. 26, 801–808 (2005)CrossRefGoogle Scholar
  7. 7.
    Patra, S., Gautam, R., Singla, A.: A novel context sensitive multilevel thresholding for image segmentations. Appl. Soft Comput. 23, 122–127 (2014)CrossRefGoogle Scholar
  8. 8.
    Rosenfeld, A., Torre, P.D.: Histogram concavity as an aid in threshold selection. IEEE Trans. Syst. Man Cybern. 13, 231–235 (1983)CrossRefGoogle Scholar
  9. 9.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classif. Wiley, Singapore (2001)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.School of Mathematics and Computer ApplicationsThapar UniversityPatialaIndia
  2. 2.Department of Computer Science and EngineeringTezpur UniversityTezpurIndia

Personalised recommendations