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Applied Sciences
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Underwater Image
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Underwater Image

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 13398

Special Issue Editor

Prof. Dr. Nikos Petrellis

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patra, Greece
Interests: image processing; classification; segmentation; disease diagnosis; plant disease; image filtering; embedded systems; mixed signal; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Images of the underwater world can play an important role in various applications in the context of biology, geology, archaeology, defense, sports, etc. More specifically, the study of living organisms at the bottom of the sea or rivers using optical imaging, measuring undersea pollution, studying weather phenomena related to the sea, lakes, or rivers, monitoring fish farms and constructions like dams and bridges, as well as activities such as laying underwater cables, monitoring undersea geological structures, employing high-tech imaging solutions for the discovery and classification of archaeological relics, monitoring the undersea environment for defense purposes, using cameras to support diving and rowing sports, and so on, are some indicative applications of underwater imaging. The related technologies include general purpose and customized cameras operating in visible or non-visible wavelengths; low-power circuits resilient to vibrations, high pressure, corrosion, and rust; underwater networks for the communication between sensors/cameras and ground stations; artificial intelligence for underwater pattern recognition; machine learning for distinguishing underwater findings; among others. Authors working in any of these areas are encouraged to submit the results of their research in the Special Issue of “Underwater Image”.

Prof. Dr. Nikos Petrellis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image processing
  • underwater cameras
  • underwater communications
  • undersea pattern recognition
  • underwater activities

Published Papers (3 papers)

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Research

23 pages, 4884 KiB  
Article
Measurement of Fish Morphological Features through Image Processing and Deep Learning Techniques
by Nikos Petrellis
Appl. Sci. 2021, 11(10), 4416; https://doi.org/10.3390/app11104416 - 13 May 2021
Cited by 25 | Viewed by 8286
Abstract
Noninvasive morphological feature monitoring is essential in fish culture, since these features are currently measured manually with a high cost. These morphological parameters can concern the size or mass of the fish, or its health as indicated, for example, by the color of [...] Read more.
Noninvasive morphological feature monitoring is essential in fish culture, since these features are currently measured manually with a high cost. These morphological parameters can concern the size or mass of the fish, or its health as indicated, for example, by the color of the eyes or the gills. Several approaches have been proposed, based either on image processing or machine learning techniques. In this paper, both of these approaches have been combined in a unified environment with novel techniques (e.g., edge or corner detection and pattern stretching) to estimate the fish’s relative length, height and the area it occupies in the image. The method can be extended to estimate the absolute dimensions if a pair of cameras is used for obscured or slanted fish. Moreover, important fish parts such as the caudal, spiny and soft dorsal, pelvic and anal fins are located. Four species popular in fish cultures have been studied: Dicentrarchus labrax (sea bass), Diplodus puntazzo, Merluccius merluccius (cod fish) and Sparus aurata (sea bream). Taking into consideration that there are no large public datasets for the specific species, the training and testing of the developed methods has been performed using 25 photographs per species. The fish length estimation error ranges between 1.9% and 13.2%, which is comparable to the referenced approaches that are trained with much larger datasets and do not offer the full functionality of the proposed method. Full article
(This article belongs to the Special Issue Underwater Image)
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20 pages, 9828 KiB  
Article
Development and Research of a Multi-Medium Motion Capture System for Underwater Intelligent Agents
by Zhongpan Zhu, Xin Li, Zhipeng Wang, Luxi He, Bin He and Shengqing Xia
Appl. Sci. 2020, 10(18), 6237; https://doi.org/10.3390/app10186237 - 08 Sep 2020
Cited by 1 | Viewed by 1841
Abstract
A multi-medium motion capture system based on markers’ visual detection is developed and experimentally demonstrated for monitoring underwater intelligent agents such as fish biology and bionic robot-fish. Considering the refraction effect between air and water, a three-dimensional (3D) reconstruction model is established, which [...] Read more.
A multi-medium motion capture system based on markers’ visual detection is developed and experimentally demonstrated for monitoring underwater intelligent agents such as fish biology and bionic robot-fish. Considering the refraction effect between air and water, a three-dimensional (3D) reconstruction model is established, which can be utilized to reconstruct the 3D coordinate of markers underwater from 2D data. Furthermore, the process of markers matching is undertaken through the multi-lens fusion perception prediction combined K-Means clustering algorithm. Subsequently, in order to track the marker of being occluded, according to the kinematics information of fish, an improved Kalman filtering algorithm is proposed. Finally, the feasibility and effectiveness of proposed system are verified through experimental results. The main models and methods in this paper can provide a reference and inspiration for measurement of underwater intelligent agents. Full article
(This article belongs to the Special Issue Underwater Image)
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16 pages, 3659 KiB  
Article
Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
by Tao Sun, Yugui Tang and Zhen Zhang
Appl. Sci. 2020, 10(16), 5670; https://doi.org/10.3390/app10165670 - 15 Aug 2020
Cited by 6 | Viewed by 2536
Abstract
Imaging through wavy air-water surface suffers from uneven geometric distortions and motion blur due to surface fluctuations. Structural information of distorted underwater images is needed to correct this in some cases, such as submarine cable inspecting. This paper presents a new structural information [...] Read more.
Imaging through wavy air-water surface suffers from uneven geometric distortions and motion blur due to surface fluctuations. Structural information of distorted underwater images is needed to correct this in some cases, such as submarine cable inspecting. This paper presents a new structural information restoration method for underwater image sequences using an image registration algorithm. At first, to give higher priority to structural edge information, a reference frame is reconstructed from the sequence frames by a combination of lucky patches chosen and the guided filter. Then an iterative robust registration algorithm is applied to remove the severe distortions by registering frames against the reference frame, and the registration is guided towards the sharper boundary to ensure the integrity of edges. The experiment results show that the method exhibits improvement in sharpness and contrast, especially in some structural information such as text. Furthermore, the proposed edge-first registration strategy has faster iteration velocity and convergence speed compared with other registration strategies. Full article
(This article belongs to the Special Issue Underwater Image)
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