Algorithm for Identifying Objects in The Relief Image Using Watershed Segmentation

Karina Auliasari(1), Mira Orisa(2),


(1) National Institute of Technology Malang
(2) National Institute of Technology Malang

Abstract

This study aims to automate the process of understanding temple relief, despite the difficulties to analyze the contents of natural images. Three preprocessing stages are develop in this research namely edge detection based on convolution (EC), edge detection based on gaussian (EG) and Hybrid which is a combination between edge detection based on convolution and gaussian. These algorithm is to support the operation of Watershed transform to segment relief images. A set of relief images obtained from several temples near Malang City are used in this experiment. Two experimental parameter are develop in order to measure the performance of these algorithm, namely number of object and quality of retrieval from segmentation result. The result of experiment show that hybrid approach deliver the best performances compare the other approaches.

Keywords

Relief, Temple, Segmentation, Watershed

Full Text:

PDF

References

G. Tochon, J.B. Feret S. Valero, R.E. Martin, D.E. Knapp, P. Salembier, J. Chanussot, and G.P. Asner. 2015 On the use of binary partition trees for the crown segmentation of tropical rainforest hyperspectral images. Remote Sensing Environment, vol. 159, pp. 318-331.

I. Isgum, M.J.N.L Benders, B. Avants, M.J. Cardoso, S.J. Counsell, E.F. Gomez, L. Gui, P.S. Huppi, K.J. Kersbergen, A. Markpoulos, A. Melbourne, P. Moeskops, C.P. Mol, M. Kuklisova-Murgasova, D. Rueckert, J.A. Schnabel, V. Srhoj-Egekher, J. Wu, S. Wang, L.S de Vries and M.A. Viergever. 2015 Evaluation of automatic neonatal brain segmentation algorithms : The NeoBrainS12 challenge. Medical Image Analysis, vol. 20, pp. 135-151.

L. Yuan, Q. Yu, C. Shen, W. Hu, and Z. Yang. 2016 New Watershed segmentation algorithm based on hybrid gradient and self-adaptive marker extraction. Proceedings of IEEE 2nd International Conference on Computer and Communications, 978-1-4673-9026-2,pp. 624-628.

A. Campbell, P. Murray, E. Yakushina, S. Marshall, and W. Ion. 2017 Automated microstructural analysis of titanium alloys using digital image processing. Proceedings of 4th International Conference recent Trends in Structural Materials (IOP Conference Series : Materials Science and Engineering 179 (2017) 012011, doi: 10.1088/1757-899X/179/012011).

A. Galibourg, J. Dumoncel, N. Telmon, A. Calvet, J. Michetti and D. Maret. 2017 Assessment of automatic segmentation of teeth using a watershed-based method. The British Institute of Radiology (available at https://doi.org/10.1259/dmfr.20170220).

T. Kavzoglu and H. Tonbul. 2017 A Comparative study of segmentation quality for multi-resolution segmentation and watershed transform. Proceedings of IEEE 8th International Conference on Recent Advances in Space Technologies (RAST 2017).

C. Crysdian. 2017 Performance measurement without ground truth to achieve optimal edge. International Journal of Image and Data Fusion, Taylor and Francis Group (available at https://doi.org/10.1080/19479832.2017.1384764).

J.B.T.M. Roerdink and A. Meijster. 2001. The watershed transform : definitions, algorithms and parallelization strategies. Fundamenta Informaticae, vol. 41, pp. 187-228.

N. Amoda and R.K. Kulkarni. 2013. Image segmentation and detection using watershed transform and region based image retrival. International Journal of Emerging Trends & Technology in Computer Science, vol. 2.

A. Chadha and N. Satam. 2013. A robust rapid approach to image segmentation with optimal thresholding and watershed transform International Journal of Computer Applications, vol. 65, no. 9.

Refbacks

  • There are currently no refbacks.




Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
Website: https://journal.unnes.ac.id/nju/index.php/sji
Email: [email protected]

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.