A novel method to detect foreground region using morphological operations with block based enhancement for underwater images

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Automation of detecting the Foreground Region (FR) or Shape of the object is essential in several computer vision, object recognition applications and poses several challenges in case of underwater images. Although Synthetic Sonar Images produce better quality images scattering of light, color distortion and poor lighting conditions are the few characteristics that effects the natural scene of the captured image. A novel technique for extracting the foreground region from a low quality underwater image is presented in this paper. We have decomposed the image in to multiple levels based on discrete wavelet transforms (DWT) for improving the sharpness or to reduce the fogginess in the image in order to get the clear image. Subsequently, to determine the sharpness of the local patches in the image a block based SSI algorithm is presented. Finally, the segmentation is performed by computing the binary gradient mask with the Sobel edge detection algorithm along with morphological operations. The proposed method is fast, extracting the accurate foreground regions and also detect the smallest particles present in the image. The results are qualitatively compared with the improved fuzzy c-means clustering (FCM), Otsu’s Threshold and FCM thresholding by considering the static background images.


  • Keywords

    Block-Based SSI; Foreground Extraction; Morphological Operations; Fuzzy Segmentation.

  • References

      [1] D. Mallet, D. Pelletier, Underwater video techniques for observing coastal marine biodiversity: a review of sixty Years of publications (1952-2012), Fish. Res. 154 (2014) 44-62 https://doi.org/10.1016/j.fishres.2014.01.019.

      [2] D. P. Struthers, A.J. Danylchuk, A.D. Wilson, and S.J. Cooke, Action cameras: Bring aquatic and fisheries research in to view, Fisheries 40 (2015) 502-512. https://doi.org/10.1080/03632415.2015.1082472.

      [3] T. Celik, T. Tardi, A novel method for SideScan sonar image segmentation, IEEE J. Oceanic Eng. 36 (3) (2011) 186-194. https://doi.org/10.1109/JOE.2011.2107250.

      [4] H.A. Meziani, F.Soltani, Decentralized fuzzy CFAR detectors in homogeneous Pearson clutter background, 8Signal Process.91 (11) (2011) 2530-2540.

      [5] A.L. Chew, T.P. Bee, C.C. Swee, Automatic detection and classification of man-made targets in side scan sonar images, in: Proceedings of 2007 Symposium on Underwater Technology and Workshop on Scientific Use of Submarine Cables and Related Technologies, 2007. https://doi.org/10.1109/UT.2007.370841.

      [6] Z. Liu, T.Xiaodong, X.Dechao, Man-made object detection algorithm of sonar image based on texture analysis, in: Proceedings of 2006 8th International Conference on Signal Processing, 2006. https://doi.org/10.1109/ICOSP.2006.346069.

      [7] P.M. Rajeshwari, et al., Multilevel Tsallis entropy based segmentation for detection of object and shadow in sonar images, in: Proceedings of 2015 IEEE International Conference on Signal Processing, Informatics and Communication and Energy Systems (SPICES), 2015. https://doi.org/10.1109/SPICES.2015.7091367.

      [8] Kwon, M. J.—Han, Y. J.—Shin, I.H.—Park, H.W: Hierarchical Fuzzy Segmentation of Brain MR Images. International Journal of Imaging Systems and Technology, Vol. 13, 2003, pp. 115–125. https://doi.org/10.1002/ima.10035.

      [9] Cheng, Ming, et al. "Global contrast based salient region detection." Pattern Analysis and Machine Intelligence, IEEE Transactions on 37.3 (2015): 569-582 https://doi.org/10.1109/TPAMI.2014.2345401.

      [10] Sandeep Mishra, Abanikanta Pattanayak et.al. “Adaptive Motion Detection for Image DE blurring in RTS Controller", International Journal of Innovative Research in Science, Engineering & Technology, Vol. 2, Issue 6, June 2013.

      [11] C. T. Vu, T. D. Phan, and D. M. Chandler, "A spectral and spatial measure of local perceived sharpness in natural Images”, IEEE Trans. On Image Process. vol. 21, no. 3, Mar. 2012. https://doi.org/10.1109/TIP.2011.2169974.

      [12] Phong V. Vu and Damon M. Chandler, "A Fast Wavelet-Based Algorithm for Global and Local Image Sharpness Estimation", IEEE Signal Processing Letters, Vol. 19, no. 7, July 2012 https://doi.org/10.1109/LSP.2012.2199980.

      [13] Sandeep Mishra, Radhanath Patra, Abanikanta Pattanayak, “Block Based Enhancement of Satellite Images using Sharpness Indexed Filtering”, IOSR Journal of Electronics and Communication Engineering, vol. 8, Nov-Dec, 2013.

      [14] R.Schettini, S.Corchs, Underwater image processing: state of the art of restoration and image enhancement Methods, EURASIP J. Adv. Signal Process. (2010), Article Number: 746052. https://doi.org/10.1155/2010/746052.

      [15] Cheng, Ming, et al. "Global contrast based salient region detection." Pattern Analysis and Machine Intelligence, IEEE Transactions on 37.3 (2015): 569-582. https://doi.org/10.1109/TPAMI.2014.2345401.

      [16] Li, Xiu, Hao, Jing, et al. “Real-time FLVh Localization with Binarized Normed Gradients.” Oceans’15 MTS/IEEE Washington DC (2015).

      [17] Liying Zheng and Kai Tian “Detection of small objects in side scan sonar images on POHMT and Tsallis entropy”, Signal Processing, January 2018, pp. 168-177. https://doi.org/10.1016/j.sigpro.2017.07.022.




Article ID: 13159
DOI: 10.14419/ijet.v7i3.13159

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.