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Segmentation of Salient Flying Objects in Complex Sky Scene using Reconstruction Morphological Operations

Sandeep 1 , M. Suresha2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-8 , Page no. 613-619, Aug-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i8.613619

Online published on Aug 31, 2018

Copyright © Sandeep, M. Suresha . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Sandeep, M. Suresha, “Segmentation of Salient Flying Objects in Complex Sky Scene using Reconstruction Morphological Operations,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.613-619, 2018.

MLA Style Citation: Sandeep, M. Suresha "Segmentation of Salient Flying Objects in Complex Sky Scene using Reconstruction Morphological Operations." International Journal of Computer Sciences and Engineering 6.8 (2018): 613-619.

APA Style Citation: Sandeep, M. Suresha, (2018). Segmentation of Salient Flying Objects in Complex Sky Scene using Reconstruction Morphological Operations. International Journal of Computer Sciences and Engineering, 6(8), 613-619.

BibTex Style Citation:
@article{Suresha_2018,
author = {Sandeep, M. Suresha},
title = {Segmentation of Salient Flying Objects in Complex Sky Scene using Reconstruction Morphological Operations},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {8 2018},
volume = {6},
Issue = {8},
month = {8},
year = {2018},
issn = {2347-2693},
pages = {613-619},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2743},
doi = {https://doi.org/10.26438/ijcse/v6i8.613619}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i8.613619}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2743
TI - Segmentation of Salient Flying Objects in Complex Sky Scene using Reconstruction Morphological Operations
T2 - International Journal of Computer Sciences and Engineering
AU - Sandeep, M. Suresha
PY - 2018
DA - 2018/08/31
PB - IJCSE, Indore, INDIA
SP - 613-619
IS - 8
VL - 6
SN - 2347-2693
ER -

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Abstract

Salient object segmentation is useful for supervised learning process. The challenging issues of this work is small flying object Segmentation in different lighting condition in the sky images using Morphological Closing and reconstruction techniques. In the proposed method, detection of regional maximal in grey level image with specified connectivity and removing of borders, filled holes. Finally, identifies flying object and gives fast processing and effective results. Experimental results on dataset demonstrates the proposed techniques performs well against existing methods.

Key-Words / Index Term

Birds, Grayscale reconstruction, Morphological operation

References

[1] A. Borji, M.M Cheng and H. Jiang, J. Li, “Salient object detection”, A benchmark, IEEE Transactions on Image Processing, 24(12), pp. 5706-5722, 2015.
[2] A. K Mishra, Y. Aloimonos and L.F Cheong, A. Kassim. “Active visual segmentation”, IEEE transactions on pattern analysis and machine intelligence, 34(4), 639-653, 2012.
[3] A. Takeki, T.T Trinh and R. Yoshihashi, R. Kawakami, M. Iida and T. Naemura, “Detection of small birds in large images by combining a deep detector with semantic segmentation”, In Image Processing (ICIP), IEEE International Conference, pp. 3977-3981, 2016.
[4] A. Toet, “Computational versus psychophysical bottom-up image saliency: A comparative evaluation study”, IEEE transactions on pattern analysis and machine intelligence, 33(11), pp. 2131-2146, 2011.
[5] B. Irving, P. Taylor and A. Todd-Pokropek, “3D segmentation of the airway tree using a morphology-based method.”, In Proc. of Second International Workshop on Pulmonary Image Analysis, pp. 297-307, 2009.
[6] C. Pisupati, L. Wolff and E. Zerhouni, W. Mitzner, “Segmentation of 3D pulmonary trees using mathematical morphology”, In Mathematical morphology and its applications to image and signal processing, pp. 409-416, 1996.
[7] D. Aykac, E.A Hoffman and G. McLennan, J.M Reinhardt, “Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images”, IEEE transactions on medical imaging, 22(8), pp. 940-950, 2003.
[8] J. Kim, D. Han and Y.W Tai, “Salient region detection via high-dimensional color transform and local spatial support”, IEEE transactions on image processing, 25(1), pp. 9-23, 2016.
[9] L. Vincent, “Morphological grayscale reconstruction in image analysis applications and efficient algorithms”, IEEE transactions on image processing, 2(2), pp. 176-201, 1993.
[10] L. Vincent, “Morphological grayscale reconstruction: definition, efficient algorithm and applications in image analysis”, In Computer Vision and Pattern Recognition, Proceedings CVPR, 1992, Computer Society Conference on IEEE, pp. 633-635, 1992.
[11] L. Wang, J. Xue and N. Zheng, G. Hua, “Automatic salient object extraction with contextual cue”, In Computer Vision (ICCV), 2011 IEEE International Conference on 2011 Nov, pp. 105-112, 2011.
[12] M. Suresh, Sandeep and T. Harisha Naik, “Recognition of salient bird objects using feed forward back propagation neural networks”, Indian Journal of Computer Engineering and Application, Vol. 06. Issue.07, pp. 1-13, 2018.
[13] M.M Cheng, N.J Mitra and X. Huang, P.H Torr, S.M Hu, “Global contrast based salient region detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), pp. 569-582, 2015.
[14] N.C Truong Hai, & H.R Park, “Extracting detected salient object by active segmentation”, In Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, pp. 35, 2011.
[15] R. Achanta, S. Hemami and F. Estrada, S. Susstrunk, “Frequency-tuned salient region detection”, International Computer vision and pattern recognition(cvpr), 2009, pp. 1597-1604,2009.
[16] W. Qi, M.M Cheng and A. Borji, H Lu, L.F Bai, “Saliency Rank: Two-stage manifold ranking for salient object detection”, Computational Visual Media, 1(4), 309-320, 2015.
[17] W. Wang, J. Shen and L. Shao, “Video salient object detection via fully convolutional networks”, IEEE Transactions on Image Processing, 27(1), pp. 38-49, 2017.
[18] Y. Li, X. Hou, and C. Koch, J.M Rehg, A.L Yuille, “The secrets of salient object segmentation”, Georgia Institute of Technology, 2014.
[19] https://unsplash.com/search/photos/flying-bird.