High-Resolution Underwater Holographic Imaging

Watson, John

doi:10.1016/b978-0-12-803581-8.09612-0

Cited by 8 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 1C shows the DHC layout. The algorithm of numerical particle image restoration from the digital hologram is well known (Schnars and Juptner, 2002;Watson, 2018) and underlies the base software. The numerical processing of digital holograms and the algorithm of parameter determination and automatic classification of particles are described in our previous works (Dyomin and Olshukov, 2016;Dyomin et al, 2017;Dyomin et al, 2018b) and form the problem-oriented software.…”

Section: Methodsmentioning

confidence: 99%

“…The sequence of operations performed using DHC and registered digital holograms is called the DHC technology. In addition to digital hologram recording, the DHC technology includes a set of numerical operations on its special processinghologram preprocessing to eliminate edge effects and to level the background noise (Dyomin and Olshukov, 2016;Dyomin et al, 2018b), restoration of holographic images of cross-sections of medium volume at various distances (Schnars and Juptner, 2002;Watson, 2018), and a 2D display of holographic volume with plankton (Dyomin et al, 2018b). The 2D display thus constructed consists of focused images of all plankton species in the studied medium volume at the time of hologram registration (Dyomin et al, 2018b).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring of Plankton Spatial and Temporal Characteristics With the Use of a Submersible Digital Holographic Camera

et al. 2020

View full text Add to dashboard Cite

This study shows that the use of a submersible digital holographic camera as part of a multifunctional hardware and software complex allows carrying out in situ measurements of plankton, automating the process of obtaining data on plankton, as well as classifying plankton species up to an order within the specified taxonomic groups. Such automation ensures monitoring expeditionary or stationary research of species diversity and spatial and temporal organization of zooplankton in conjunction with the hydrophysical parameters of the medium. This paper presents the full-scale results of vertical profiles and daily measurements of plankton made with the use of the submersible digital holographic camera as well as the classification of plankton in laboratory and field conditions in the automatic mode. It is shown that, within the accomplished version, the classification algorithm using the morphological parameter makes it possible to solve the problem quickly (the time required to obtain the result is less than 1 s and depends on the number of plankton particles and the frame size of a restored image); however, the classification accuracy by orders varies within 50-60%.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Monitoring of Plankton Spatial and Temporal Characteristics With the Use of a Submersible Digital Holographic Camera

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The mean (pixel mean of images) subtraction of the raw images processing can eliminate static background noise [16]. Meanwhile, for the reconstructed image, the raw holographic images and the focus images, there are a large number of mean subtraction processing [17]. Therefore, the effect of the mean subtraction processing on the image detection and recognition results needs to be experimentally demonstrated.…”

Section: Experiments Of Mean Subtraction On Recognitionmentioning

confidence: 99%

Study on Holographic Image Recognition Technology of Zooplankton

Shi¹,

Wang²,

Cao³

et al. 2019

dtcse

View full text Add to dashboard Cite

Marine zooplankton has important ecological and economic value. The observation and automatic image recognition technology of marine zooplankton is an important mean to acquire data such as species, quantity, spatial distribution and behavioral postures of zooplankton, and is an important support for marine scientific research. Digital holography has an innate advantage of refocusing and reconstruction, which is suitable for deep learning and living zooplankton recognition. In this study, a large number of holographic images was trained by using the improved YOLOv2 model, and after test, the study achieved satisfactory results: the models trained by the images with sharpness assessment score of 0.6 or higher, have precision rate above 94% and a recall rate above 88%. This study mainly discusses: (1) the detection method of moving targets to acquire the images of moving zooplankton; (2) the two factors that affect the holographic images recognition results, mean (pixel mean of images) subtraction operation and image sharpness, and the no-reference sharpness assessment based on structural similarity for holographic images; (3) the relationship between sharpness assessment index or mean subtraction and the recognition results.

show abstract

“…One of the major issues remaining for development of global indicators from zooplankton variables is the compatibility of data that are collected, processed and analysed using project-specific sampling gears and analytical methods. There is an increasing demand to develop new sensor technology to enable autonomous measurement of taxonomic or functional level information of zooplankton data ( Le Bourg et al , 2014 ; Watson, 2018 ). Once these technologies become matured and available to the international observation community, zooplankton biodiversity information can be collected with standardized methods using various existing observation platforms such as buoys, gliders and moorings.…”

Section: Global Zooplankton Indicators For the Aichi Targetsmentioning

confidence: 99%

Zooplankton monitoring to contribute towards addressing global biodiversity conservation challenges

Chiba

Batten²,

Martin

et al. 2018

Journal of Plankton Research

View full text Add to dashboard Cite

Oceanographers have an increasing responsibility to ensure that the outcomes of scientific research are conveyed to the policy-making sphere to achieve conservation and sustainable use of marine biodiversity. Zooplankton monitoring projects have helped to increase our understanding of the processes by which marine ecosystems respond to climate change and other environmental variations, ranging from regional to global scales, and its scientific value is recognized in the contexts of fisheries, biodiversity and global change studies. Nevertheless, zooplankton data have rarely been used at policy level for conservation and management of marine ecosystems services. One way that this can be pragmatically and effectively achieved is via the development of zooplankton indicators, which could for instance contribute to filling in gaps in the suite of global indicators to track progress against the Aichi Biodiversity Targets of the United Nations Strategic Plan for Biodiversity 2010–2020. This article begins by highlighting how under-represented the marine realm is within the current suite of global Aichi Target indicators. We then examine the potential to develop global indicators for relevant Aichi Targets, using existing zooplankton monitoring data, to address global biodiversity conservation challenges.

show abstract

High-Resolution Underwater Holographic Imaging

Cited by 8 publications

References 8 publications

Monitoring of Plankton Spatial and Temporal Characteristics With the Use of a Submersible Digital Holographic Camera

Monitoring of Plankton Spatial and Temporal Characteristics With the Use of a Submersible Digital Holographic Camera

Study on Holographic Image Recognition Technology of Zooplankton

Zooplankton monitoring to contribute towards addressing global biodiversity conservation challenges

Contact Info

Product

Resources

About