Abstract
Aiming at two problems from the process of target and background segmentation in the field of manganese nodule image processing, the uneven illumination and the morphological defects of manganese nodules caused by white sand cover, this paper proposes an improved method. After the image is processed by this method, the above two problems are solved and segmentable manganese nodule images are obtained. Finally, the processed image is segmented to verify the feasibility and stability of the method. The original method is only used in document images processing. It is not suitable for the processing of manganese nodule images because it can’t repair the morphology of manganese nodules and reduce the contrast between the target and the background. The image of manganese nodules has the following characteristics: the pixels with higher gray value of white sand are scattered around the pixels with lower gray value of manganese nodules, and the area covered by white sand is smaller. In view of this feature, controllable artifacts are used to repair the morphology of manganese nodules. First, the image is preprocessed, and the background gray value is calculated and subtracted from the original image. Then the gray value of the image is adjusted by the block diagram. Finally, by performing the above operation again, the problem of uneven illumination in the image can be solved, and the morphology of manganese nodules in the image can be repaired without affecting the gap between them The simulation results show that, compared with the original method and other methods, several images processed by this method have significant effect in the process of segmentation, which proves that the method is effective and stable.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhuang, W., Chen, Y., Su, J., Wang, B., et al.: Design of human activity recognition algorithms based on a single wearable IMU sensor. Int. J. Sens. Netw. 30(3), 193–206 (2019)
Zhuang, W., Sun, X., Zhi, Y., et al.: A novel wearable smart button system for fall detectio. In: 2017 AIP Conference Proceedings, vol. 1838(1), pp. 020–035 (2017)
Awad, NH, Ali, M.Z., Suganthan, P.N.: Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems. In: 2017 IEEE Congress on Evolutionary Computation, San Sebastian, Spain, pp. 372–379 (2017)
Petit, F., Capelle-Laize, A.S., Carre, P.: Underwater image enhancement by attenuation inversion with quaternions. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Taiwan, vol. 200, pp. 1177–1180 (2009)
Sahu, P., Gupta, N., Sharma, N.: A survey on underwater image enhancement techniques. Int. J. Comput. Appl. 87(13), 19–23 (2014)
Huang, D., Wang, Y., Song, W., et al.: Underwater image enhancement method using adaptive histogram stretching in different color models. J. Image Graph. 23(5), 640–651 (2018)
Badekas, E., Papamarkos, N.: Estimation of appropriate parameter values for document binarization techniques. Int. J. Robot. Autom. 24(1), 66–78 (2009)
Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000)
Hassan, N.M., Mostfa, E., Salehi, M.E.: A fast fault-tolerant architecture for sauvola local image thresholding algorithm using stochastic computing. IEEE Trans. Very Large Scale Integr. Syst. 24(2), 808–812 (2016)
He, Z.: Binarization of document image captured under uneven illumination. J. Shanghai Univ. Eng. Sci. 25(2), 164–166 (2011)
Wang, G., Dong, Q., Pan, Z.: Retinex theory based active contour model for segmentation of inhimogeneous images. Digit. Process. 50, 43–50 (2016)
Diels, E., van Dael, M., Keresztes, J., et al.: Assessmennt of bruise volumes in apples using X-ray computed tomography. Post-Harvest Biol. Technol. 128, 24–32 (2017)
Liu, Y., Zhou, T., Wang, X., et al.: High precision pose calibration method of multi-line camera system. J. Huazhong Univ. Sci. Technol. (Nat. Sci. Edn.) 46(11), 81–86 (2018)
Wang, Q., Wu, K., Wang, X., et al.: Automatic detection and classification of foreign bodies of dumplings based on x-ray. J. Comput.-Aided Des. Comput. Graph. 30(12), 2242–2252 (2018)
Jing, J., Guo, G.: Yarn packages hairiness detection based on machine vision. J. Text. Res. 40(1), 148–152 (2019)
Kaur, R.: Modified watershed transform using convolution filtering for image segmentation. Int. J. Eng. Sci. Res. Technol. 3(8), 449–454 (2014)
Qun, M., Heng, Y., Fang, C., et al.: Reversible data hiding in encrypted image based on block classification permutation. Comput. Mater. Continua 59(1), 119–133 (2019)
Niu, B.H., Huang, Y.F.: An improved method for web text affective cognition computing based on knowledge graph. Comput. Mater. Continua 59(1), 1–14 (2019)
Wang, Q., Yang, C.G., Wu, S.E., et al.: Lever arm compensation of autonomous underwater vehicle for fast transfer alignment. Comput. Mater. Continua 59(1), 105–118 (2019)
Acknowledgements
This work was supported by Open Fund Project of China Key Laboratory of Submarine Geoscience (KLSG1802), Science & Technology Project of China Ocean Mineral Resources Research and Development Association (DY135-N1-1-05).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mao, Hd., Liu, Yl., Yan, Hz., Qian, C. (2020). Image Segmentation of Manganese Nodules Based on Background Gray Value Computation. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_55
Download citation
DOI: https://doi.org/10.1007/978-3-030-57884-8_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57883-1
Online ISBN: 978-3-030-57884-8
eBook Packages: Computer ScienceComputer Science (R0)