Journal article  Open Access

The use of saliency in underwater computer vision: a review

Reggiannini M., Moroni D.

Underwater computer vision  visual saliency  underwater computer vision  underwater image understanding  Multi-sensor survey  Visual saliency  Underwater image understanding  General Earth and Planetary Sciences  multi-sensor survey  [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 

Underwater survey and inspection are tasks of paramount relevance for a variety of applications. They are usually performed through the employment of optical and acoustic sensors installed aboard underwater vehicles, in order to capture details of the surrounding environment. The informative properties of the data are systematically affected by a number of disturbing factors, such as the signal energy absorbed by the propagation medium or diverse noise categories contaminating the resulting imagery. Restoring the signal properties in order to exploit the carried information is typically a tough challenge. Visual saliency refers to the computational modeling of the preliminary perceptual stages of human vision, where the presence of conspicuous targets within a surveyed scene activates neurons of the visual cortex, specifically sensitive to meaningful visual variations. In relatively recent years, visual saliency has been exploited in the field of automated underwater exploration. This work provides a comprehensive overview of the computational methods implemented and applied in underwater computer vision tasks, based on the extraction of visual saliency-related features.

Source: Remote sensing (Basel) 13 (2020). doi:10.3390/rs13010022

Publisher: Molecular Diversity Preservation International, Basel


1. Borji, A.; Itti, L. State-of-the-Art in Visual Attention Modeling. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 35, 185-207.
2. Duntley, S.Q. Light in the sea. JOSA 1963, 53, 214-233.
3. Chiang, J.Y.; Chen, Y.C. Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans. Image Process. 2011, 21, 1756-1769.
4. Galdran, A.; Pardo, D.; Picón, A.; Alvarez-Gila, A. Automatic red-channel underwater image restoration. J. Vis. Commun. Image Represent. 2015, 26, 132-145.
5. Li, C.; Quo, J.; Pang, Y.; Chen, S.; Wang, J. Single underwater image restoration by blue-green channels dehazing and red channel correction. In Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 20-25 March 2016; pp. 1731-1735.
6. Łuczy n´ski, T.; Birk, A. Underwater image haze removal with an underwater-ready dark channel prior. In Proceedings of the IEEE OCEANS 2017-Anchorage, Anchorage, AK, USA, 18-21 September 2017; pp. 1-6.
7. Richards, M.A.; Scheer, J.A.; Holm, W.A.; Beckley, B.; Mark, P.; Richards, A. (Eds.) Principles of Modern Radar: Basic Principles; Institution of Engineering and Technology: London, UK, 2010.
8. Itti, L.; Koch, C.; Niebur, E. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 1998, 20, 1254-1259.
9. Frintrop, S.; Rome, E.; Christensen, H.I. Computational visual attention systems and their cognitive foundations: A survey. ACM Trans. Appl. Percept. (TAP) 2010, 7, 1-39.
10. Frintrop, S. Computational visual attention. In Computer Analysis of Human Behavior; Springer: Berlin, Germany, 2011; pp. 69-101.
11. Treisman, A.M.; Gelade, G. A feature-integration theory of attention. Cogn. Psychol. 1980, 12, 97-136, doi:10.1016/0010- 0285(80)90005-5.
12. Chen, Z.; Gao, H.; Zhang, Z.; Zhou, H.; Wang, X.; Tian, Y. Underwater salient object detection by combining 2d and 3d visual features. Neurocomputing 2020, 391, 249-259.
13. Marshall, J.; Carleton, K.L.; Cronin, T. Colour vision in marine organisms. Curr. Opin. Neurobiol. 2015, 34, 86-94.
14. Hou, X.; Zhang, L. Saliency detection: A spectral residual approach. In Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 18-23 June 2007; pp. 1-8.
15. Feng, H.; Yin, X.; Xu, L.; Lv, G.; Li, Q.; Wang, L. Underwater salient object detection jointly using improved spectral residual and Fuzzy c-Means. J. Intell. Fuzzy Syst. 2019, 37, 329-339.
16. Li, J.; Levine, M.D.; An, X.; Xu, X.; He, H. Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 35, 996-1010.
17. Ell, T.A. Quaternion-Fourier transforms for analysis of two-dimensional linear time-invariant partial differential systems. In Proceedings of the 32nd IEEE Conference on Decision and Control, San Antonio, TX, USA, 15-17 December 1993; pp. 1830-1841.
18. Guo, C.; Zhang, L. A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Process. 2009, 19, 185-198.
19. Kadir, T.; Brady, M. Saliency, Scale and Image Description. Int. J. Comput. Vis. 2001, 45, 83-105, doi:10.1023/A:1012460413855.
20. Edgington, D.R.; Salamy, K.A.; Risi, M.; Sherlock, R.; Walther, D.; Koch, C. Automated event detection in underwater video. In Oceans 2003. Celebrating the Past... Teaming Toward the Future (IEEE Cat. No. 03CH37492); IEEE: New York, NY, USA, 2003; Volume 5, pp. P2749-P2753.
21. Ahn, J.; Nishida, Y.; Ishii, K.; Ura, T. A Sea Creatures Classification Method using Convolutional Neural Networks. In Proceedings of the 2018 18th International Conference on Control, Automation and Systems (ICCAS), PyeongChang, Korea, 17-20 October 2018; pp. 420-423.
22. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet Classification with Deep Convolutional Neural Networks. Commun. ACM 2017, 60, 84-90, doi:10.1145/3065386.
23. Atallah, L.; Shang, C.; Bates, R. Object detection at different resolution in archaeological side-scan sonar images. Eur. Ocean. 2005, 1, 287-292.
24. Wang, H.; Dong, X.; Jie, S.; Wu, X.; Chen, Z. Saliency-Based Adaptive Object Extraction for Color Underwater Images. Appl. Mech. Mater. 2013, 347-350, doi:10.2991/iccsee.2013.661.
25. Bhattacharyya Distance. Encyclopedia of Mathematics. 2020. Available online: http://encyclopediaofmath.org/index.php?title= Bhattacharyya_distance&oldid=46047 (accessed on 21 December 2020).
26. Huo, G.; Wu, Z.; Li, J.; Li, S. Underwater Target Detection and 3D Reconstruction System Based on Binocular Vision. Sensors 2018, 18, 3570, doi:10.3390/s18103570.
27. Chen, Z.; Wang, H.; Shen, J.; Dong, X. Underwater Object Detection by Combining the Spectral Residual and Three-Frame Algorithm. In Advances in Computer Science and its Applications; Jeong, H.Y., Obaidat, M.S., Yen, N.Y., Park, J.J.J.H., Eds.; Springer: Berlin/Heidelberg, Germany, 2014; pp. 1109-1114.
28. Bezdek, J.C. Pattern Recognition with Fuzzy Objective Function Algorithms; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2013.
29. Kumar, N.; Sardana, H.; Shome, S.; Mittal, N. Saliency Subtraction Inspired Automated Event Detection in Underwater Environments. Cogn. Comput. 2019, 12, doi:10.1007/s12559-019-09671-x.
30. Gonzalez, R.C.; Woods, R.E. Digital Image Processing, 4th ed.; Pearson: London, UK; Prentice Hall: Upper Saddle River, NY, USA, 2018.
31. Underwater moving object detection by temporal information. Int. J. Recent Technol. Eng. (IJRTE) 2019, 8.
32. Cong, Y.; Fan, B.; Hou, D.; Fan, H.; Liu, K.; Luo, J. Novel Event Analysis for Human-Machine Collaborative Underwater Exploration. Pattern Recognit. 2019, 96, 106967, doi:10.1016/j.patcog.2019.106967.
33. Hou, Q.; Cheng, M.M.; Hu, X.; Borji, A.; Tu, Z.; Torr, P.H. Deeply supervised salient object detection with short connections. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21-26 July 2017; pp. 3203-3212.
34. Zhu, Y.; Hao, B.; Jiang, B.; Nian, R.; He, B.; Ren, X.; Lendasse, A. Underwater image segmentation with co-saliency detection and local statistical active contour model. In Proceedings of the OCEANS 2017-Aberdeen, Aberdeen, UK, 19-22 June 2017; pp. 1-5.
35. Barat, C.; Phlypo, R. A fully automated method to detect and segment a manufactured object in an underwater color image. EURASIP J. Adv. Signal Process. 2010, 2010, 1-10.
36. Kumar, N.; Sardana, H.; Shome, S. Saliency based shape extraction of objects in unconstrained underwater environment. Multimed. Tools Appl. 2018, 78, doi:10.1007/s11042-018-6849-9.
37. Paragios, N.; Deriche, R. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 266-280.
38. Barnes, C.; Best, M.; Bornhold, B.; Juniper, S.; Pirenne, B.; Phibbs, P. The NEPTUNE Project-a cabled ocean observatory in the NE Pacific: Overview, challenges and scientific objectives for the installation and operation of Stage I in Canadian waters. In Proceedings of the IEEE 2007 Symposium on Underwater Technology and Workshop on Scientific Use of Submarine Cables and Related Technologies, Tokyo, Japan, 17-20 April 2007; pp. 308-313.
39. Gebali, A.; Albu, A.B.; Hoeberechts, M. Detection of salient events in large datasets of underwater video. In Proceedings of the 2012 Oceans, Hampton Roads, VA, USA, 14-19 October 2012; pp. 1-10, doi:10.1109/OCEANS.2012.6404996.
40. Chen, Z.; Sun, Y.; Gu, Y.; Wang, H.; Qian, H.; Zheng, H. Underwater Object Segmentation Integrating Transmission and Saliency Features. IEEE Access 2019, 7, 72420-72430.
41. He, K.; Sun, J.; Tang, X. Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 33, 2341-2353.
42. Sathya, R.; Bharathi, M.; Dhivyasri, G. Underwater image enhancement by dark channel prior. In Proceedings of the IEEE 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India, 26-27 February 2015; pp. 1119-1123.
43. Vese, L.A.; Chan, T.F. A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vis. 2002, 50, 271-293.
44. Harel, J.; Koch, C.; Perona, P. Graph-based visual saliency. In Advances in Neural Information Processing Systems; MIT Press: Cambridge, UK, 2007; pp. 545-552.
45. Gu, K.; Zhai, G.; Lin, W.; Yang, X.; Zhang, W. Visual saliency detection with free energy theory. IEEE Signal Process. Lett. 2015, 22, 1552-1555.
46. Erdem, E.; Erdem, A. Visual saliency estimation by nonlinearly integrating features using region covariances. J. Vis. 2013, 13, 11-11.
47. Johnson-Roberson, M.; Pizarro, O.; Williams, S. Saliency ranking for benthic survey using underwater images. In Proceedings of the 2010 11th International Conference on Control Automation Robotics Vision, Singapore, 7-10 December 2010; pp. 459-466.
48. Chuang, M.; Hwang, J.; Williams, K. A Feature Learning and Object Recognition Framework for Underwater Fish Images. IEEE Trans. Image Process. 2016, 25, 1862-1872.
49. Boom, B.J.; Huang, P.X.; He, J.; Fisher, R.B. Supporting ground-truth annotation of image datasets using clustering. In Proceedings of the IEEE 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan, 11-15 November 2012; pp. 1542-1545.
50. Zhu, J.; Siquan, Y.; Han, Z.; Tang, Y.; Wu, C. Underwater Object Recognition Using Transformable Template Matching Based on Prior Knowledge. Math. Probl. Eng. 2019, 2019, 1-11, doi:10.1155/2019/2892975.
51. Jian, M.; Qi, Q.; Dong, J.; Sun, X.; Sun, Y.; Lam, K.M. Saliency detection using quaternionic distance based weber local descriptor and level priors. Multimed. Tools Appl. 2018, 77, 14343-14360.
52. Lan, R.; Zhou, Y.; Tang, Y.Y. Quaternionic weber local descriptor of color images. IEEE Trans. Circuits Syst. Video Technol. 2015, 27, 261-274.
53. Jian, M.; Qi, Q.; Dong, J.; Yin, Y.; Lam, K.M. Integrating QDWD with pattern distinctness and local contrast for underwater saliency detection. J. Vis. Commun. Image Represent. 2018, 53, 31-41.
54. Margolin, R.; Tal, A.; Zelnik-Manor, L. What makes a patch distinct? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 23-28 June 2013; pp. 1139-1146.
55. Yang, C.; Zhang, L.; Lu, H. Graph-regularized saliency detection with convex-hull-based center prior. IEEE Signal Process. Lett. 2013, 20, 637-640.
56. Harrison, R.; Birchall, R.; Mann, D.; Wang, W. A novel ensemble of distance measures for feature evaluation: Application to sonar imagery. In International Conference on Intelligent Data Engineering and Automated Learning; Springer: Berlin, Germany, 2011; pp. 327-336.
57. Durrant-Whyte, H.; Bailey, T. Simultaneous localization and mapping: Part I. IEEE Robot. Autom. Mag. 2006, 13, 99-110.
58. Kim, A.; Eustice, R. Combined visually and geometrically informative link hypothesis for pose-graph visual SLAM using bag-of-words. In Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA, 25-30 September 2011; pp. 1647-1654, doi:10.1109/IROS.2011.6048439.
59. Kim, A.; Eustice, R.M. Real-Time Visual SLAM for Autonomous Underwater Hull Inspection Using Visual Saliency. IEEE Trans. Robot. 2013, 29, 719-733.
60. Lowe, D.G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 2004, 60, 91-110.
61. Bay, H.; Ess, A.; Tuytelaars, T.; Van Gool, L. Speeded-up robust features (SURF). Comput. Vis. Image Underst. 2008, 110, 346-359.
62. Kim, A.; Eustice, R.M. Active visual SLAM for robotic area coverage: Theory and experiment. Int. J. Robot. Res. 2015, 34, 457-475.
63. Ozog, P.; Carlevaris-Bianco, N.; Kim, A.; Eustice, R. Long-term Mapping Techniques for Ship Hull Inspection and Surveillance using an Autonomous Underwater Vehicle. J. Field Robot. 2015, 24, doi:10.1002/rob.21582.
64. Geng, Y.; Wang, Z.; Shi, C.; Nian, R.; Zhang, C.; He, B.; Shen, Y.; Lendasse, A. Seafloor visual saliency evaluation for navigation with BoW and DBSCAN. In Proceedings of the OCEANS 2016-Shanghai, Shanghai, China, 10-13 April 2016; pp. 1-5.
65. Ester, M.; Kriegel, H.P.; Sander, J.; Xu, X. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In KDD'96, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA, 2-4 August 1996; AAAI Press: Palo Alto, CA, USA, 1996; pp. 226-231.
66. Li, J.; Kaess, M.; Eustice, R.M.; Johnson-Roberson, M. Pose-Graph SLAM Using Forward-Looking Sonar. IEEE Robot. Autom. Lett. 2018, 3, 2330-2337.
67. Kaeli, J. Real-Time Anomaly Detection in Side-Scan Sonar Imagery for Adaptive AUV Missions; In Proceedings of the 2016 IEEE/OES Autonomous Underwater Vehicles (AUV), Tokyo, Japan, 6-9 November 2016; pp. 85-89, doi: 10.1109/AUV.2016.7778653.
68. Chailloux, C. Region of interest on sonar image for non symbolic registration. In Proceedings of OCEANS 2005 MTS/IEEE, Washington, DC, USA, 17-23 September 2005; pp. 810-814.
69. Harris, C.G.; Stephens, M.; others. A combined corner and edge detector. In Proceedings of the Fourth Alvey Vision Conference, Manchester, UK, 31 August-2 September 1988; pp. 147-151, doi: 10.5244/c.2.23.
70. Chauvin, A.; Hérault, J.; Marendaz, C.; Peyrin, C. Natural scene perception: Visual attractors and image neural computation and psychology. In Connectionist Models of Cognition and Perception, Proceedings of the Seventh Neural Computation and Psychology Workshop, Brighton, UK, 17-19 September 2001; Bullinaria, J.A., Lowe, W., Eds.; World Scientific Publishing Co Pte Ltd.: Singapore, 2002.
71. Chailloux, C.; Le Caillec, J.; Gueriot, D.; Zerr, B. Intensity-Based Block Matching Algorithm for Mosaicing Sonar Images. IEEE J. Ocean. Eng. 2011, 36, 627-645.
72. Mitchell, H.B. Image Fusion; Springer: Berlin/Heidelberg, Germany, 2010.
73. Fu, L.; Wang, Y.; Zhang, Z.; Nian, R.; Yan, T.; Lendasse, A. A shadow-removal based saliency map for point feature detection of underwater objects. In Proceedings of the OCEANS 2015-MTS/IEEE Washington, Washington, DC, USA, 19-22 October 2015; pp. 1-5.
74. Zhang, L.; He, B.; Song, Y.; Yan, T. Underwater image feature extraction and matching based on visual saliency detection. In Proceedings of the OCEANS 2016-Shanghai, Shanghai, China, 10-13 April 2016; pp. 1-4.
75. Johnson-Roberson, M.; Bryson, M.; Douillard, B.; Pizarro, O.; Williams, S. Crowdsourced Saliency for Mining Robotically Gathered 3D Maps Using Multitouch Interaction on Smartphones and Tablets; In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 31 May-5 June 2014; IEEE: New York, NY, USA, 2014; pp. 6032-6039, doi:10.1109/ICRA.2014.6907748.
76. Johnson-Roberson, M.; Bryson, M.; Douillard, B.; Pizarro, O.; Williams, S. Discovering salient regions on 3D photo-textured maps: Crowdsourcing interaction data from multitouch smartphones and tablets. Comput. Vis. Image Underst. 2015, 131, 28-41, doi:10.1016/j.cviu.2014.07.006.
77. Rabiner, L.R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 1989, 77, 257-286.
78. Achanta, R.; Hemami, S.; Estrada, F.; Susstrunk, S. Frequency-tuned salient region detection. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20-25 June 2009; pp. 1597-1604.
79. Fang, S.; Deng, R.; Cao, Y.; Fang, C. Effective Single Underwater Image Enhancement by Fusion. J. Comput. 2013, 8, doi:10.4304/jcp.8.4.904-911.
80. Singh, R.; Biswas, M. Adaptive histogram equalization based fusion technique for hazy underwater image enhancement. In Proceedings of the 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Tamil Nadu, India, 15-17 December 2016; pp. 1-5.
81. Ancuti, C.; Ancuti, C.O.; De Vleeschouwer, C.; Garcia, R.; Bovik, A.C. Multi-scale underwater descattering. In Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, 4-8 December 2016; pp. 4202-4207.
82. Wang, J.; Wang, H.; Gao, G.; Lu, H.; Zhang, Z. Single Underwater Image Enhancement Based on Lp-norm Decomposition. IEEE Access 2019, 1, doi:10.1109/ACCESS.2019.2945576.
83. Forbes, T.; Goldsmith, M.; Mudur, S.; Poullis, C. DeepCaustics: Classification and Removal of Caustics From Underwater Imagery. IEEE J. Ocean. Eng. 2019, 44, 728-738.
84. Autodesk Maya. 2020. Available online: https://www.autodesk.com/products/maya/ (accessed on 21 December 2020).
85. Zhang, H.; Li, S.; Chen, W.; Liu, Y. The Influence of Different Saliency on Full-Reference Sonar Image Quality Evaluation. IOP Conf. Ser. Mater. Sci. Eng. 2019, 569, 052093, doi:10.1088/1757-899x/569/5/052093.
86. Liu, T.; Sun, J.; Zheng, N.; Tang, X.; Shum, H. Learning to Detect A Salient Object. In Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 18-23 June 2007; pp. 1-8.
87. Jian, M.; Qi, Q.; Dong, J.; Yin, Y.; Zhang, W.; Lam, K.M. The OUC-vision large-scale underwater image database. In Proceedings of the 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China, 10-14 July 2017; pp. 1297-1302.
88. Jian, M.; Qi, Q.; Yu, H.; Dong, J.; Cui, C.; Nie, X.; Zhang, H.; Yin, Y.; Lam, K.M. The extended marine underwater environment database and baseline evaluations. Appl. Soft Comput. 2019, 80, 425-437.
89. Li, X.; Lu, H.; Zhang, L.; Ruan, X.; Yang, M.H. Saliency detection via dense and sparse reconstruction. In Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia, 2-8 December 2013; pp. 2976-2983.
90. Tong, N.; Lu, H.; Zhang, L.; Ruan, X. Saliency detection with multi-scale superpixels. IEEE Signal Process. Lett. 2014, 21, 1035-1039.
91. Qin, Y.; Lu, H.; Xu, Y.; Wang, H. Saliency detection via cellular automata. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7-12 June 2015; pp. 110-119.
92. Jian, M.; Qi, Q. Underwater Images Part A. 2019. Available online: https://zenodo.org/record/2542305#.X-INxNhKiUk (accessed on 21 December 2020).
93. Jian, M.; Qi, Q. Underwater Images Part B. 2019. Available online: https://zenodo.org/record/2542307#.X-INxdhKiUk (accessed on 21 December 2020).
94. Li, C.; Guo, C.; Ren, W.; Cong, R.; Hou, J.; Kwong, S.; Tao, D. An underwater image enhancement benchmark dataset and beyond. IEEE Trans. Image Process. 2019, 29, 4376-4389.
95. Cui, Z.; Wu, J.; Yu, H.; Zhou, Y.; Liang, L. Underwater Image Saliency Detection Based on Improved Histogram Equalization. In International Conference of Pioneering Computer Scientists, Engineers and Educators; Springer: Berlin, Germany, 2019; pp. 157-165.
96. Boom, B.J.; He, J.; Palazzo, S.; Huang, P.X.; Beyan, C.; Chou, H.M.; Lin, F.P.; Spampinato, C.; Fisher, R.B. A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage. Ecol. Inform. 2014, 23, 83-97.
97. Li, G.; Yu, Y. Visual saliency detection based on multiscale deep CNN features. IEEE Trans. Image Process. 2016, 25, 5012-5024.
98. Huang, K.; Gao, S. Image saliency detection via multi-scale iterative CNN. Vis. Comput. 2020, 36, 1355-1367.
99. Sanchez-Torres, G.; Ceballos-Arroyo, A.; Robles-Serrano, S. Automatic measurement of fish weight and size by processing underwater hatchery images. Eng. Lett. 2018, 26, 461-472.

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BibTeX entry
	title = {The use of saliency in underwater computer vision: a review},
	author = {Reggiannini M. and Moroni D.},
	publisher = {Molecular Diversity Preservation International, Basel  },
	doi = {10.3390/rs13010022},
	journal = {Remote sensing (Basel)},
	volume = {13},
	year = {2020}