Implementation of Color Image Segmentation Using Watershed Algorithm

M. Aravind Kumar*, Srinivas Bachu**
* Assistant Professor, Department of Electronics and Communication Engineering, Grandhi Varalakshmi Venkata Rao Institute of Technology, Andhra Pradesh, India.
** Associate Professor, Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus (Autonomous), Telangana, India.
Periodicity:October - December'2016

Abstract

The objective of this paper is to improve the picture by utilizing division procedure for shading pictures. The most essential characteristic of division of a picture is its luminance, plentiful for a monochrome picture and shading parts of a shading picture. This paper proposes a shading based division strategy for utilization of watershed method. The watershed calculation can perform picture division utilizing numerical morphology. The individualization of an article from an advanced picture is a typical issue in the field of picture preparing. The authors introduced a versatile concealing and a thresholding instrument over every shading channel to overcome over division issue, before consolidating the division from every channel into the last one. The proposed strategy guarantees precision and nature of the shading pictures. The exploratory results are acquired utilizing Image Quality Assessment (IQA) measurements, for example, PSNR, MSE, and Color Image Quality Measure (CQM). The watershed change consolidated with a quick calculation based and topological angle approach gives great results. The numerical tests acquired outline of the effectiveness of the methodology for picture division. This methodology along these lines give a practical new answer for picture division, which might be useful in picture recovery. The trial results illuminate the viability of our way to deal with enhanced division quality in parts of accuracy and computational time. The reenactment results exhibit that the proposed calculation is promising.

Keywords

Watershed Algorithm, Image Segmentation, Peak Signal to Noise Ratio, Mean Square Error.

How to Cite this Article?

Kumar, M. A., and Bachu, S. (2016). Implementation Of Color Image Segmentation Using Watershed Algorithm. i-manager's Journal on Image Processing, 3(4), 1-8.

References

[1]. Muthukannan K, and Merlin Moses. M, (2010). “Color Image Segmentation using K-means Clustering and Optimal Fuzzy C-Means Clustering”. Proceedings of the International Conference on Communication and Computational Intelligence – 2010, Kongu Engineering College, Perundurai, Erode, T.N., India. pp.229-234.
[2]. Chinki Chandhok, Soni Chaturvedi, and A. Akhurshid, (2012). “An Approach to Image Segmentation using K means Clustering Algorithm”. International Journal of Information Technology (IJIT), Vol. 1, No. 1, pp. 11-17.
[3]. Digital image processing 3rd Edition (DIP/3e), by Gonzalez and Woods.
[4]. F. Destrempes, J.F. Angers, and M. Mignotte, (2006). “Fusion of hidden Markov random field models and its Bayesian estimation”. IEEE Trans. Image Process., Vol. 15, No. 10, pp. 2920–2935.
[5]. J.A Hartigan, (1975). Clustering Algorithms, New York, Wiley.
[6]. S.P. Lloyd, (1982). “Least squares quantization in PCM”. IEEE Trans. Inf. Theory, Vol. 28, No. 2, pp. 129–136.
[7]. J. Besag, (1986). “On the statistical analysis of dirty pictures”. J. Roy. Statist. Soc. B, Vol. 48, pp. 259–302.
[8]. S. Zhu and A. Yuille, (1996). “Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation”. IEEE Trans. Pattern Anal. Mach. Intell., Vol. 18, No. 9, pp. 884–900.
[9]. S. Mary Praveena, and Ila Vennila, (2010). “Optimization Fusion Approach for Image Segmentation using K-Means Algorithm”. International Journal of Computer Applications, Vol. 2, No. 7.
[10]. Seber, G.A.F. (1984). Multivariate Observations. Hoboken, NJ: John Wiley & Sons, Inc., 1984.
[11]. Spath, H., (1985). Cluster Dissection and Analysis: Theory, Fortran Programs, Examples. (Translated by J. Goldschmidt). New York: Halsted Press.
[12]. A.M Uso, F. Pla, and P.G Sevila, (2006). “Unsupervised Image Segmentation using a Hierarchical Clustering Selection Process”. Structural Syntactic and Statistical Pattern Recognition, Vol. 4, pp. 799-807.
[13]. A.Z Arifin, and A. Asano, (2006). “Image Segmentation by histogram thresholding using hierarchical cluster analysis”. Pattern Recognition Letters, Vol. 27, No. 13, pp. 1515-1521.
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