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Journal articles on the topic 'Plankton image classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Tang, Xiaoou, Feng Lin, Scott Samson, and Andrew Remsen. "Binary Plankton Image Classification." IEEE Journal of Oceanic Engineering 31, no. 3 (2006): 728–35. http://dx.doi.org/10.1109/joe.2004.836995.

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2

Barazanchi, Hussein Al, Abhishek Verma, and Shawn X. Wang. "Intelligent plankton image classification with deep learning." International Journal of Computational Vision and Robotics 8, no. 6 (2018): 561. http://dx.doi.org/10.1504/ijcvr.2018.095584.

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3

Wang, Shawn X., Abhishek Verma, and Hussein Al Barazanchi. "Intelligent plankton image classification with deep learning." International Journal of Computational Vision and Robotics 8, no. 6 (2018): 561. http://dx.doi.org/10.1504/ijcvr.2018.10016426.

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4

González, Pablo, Eva Álvarez, Jorge Díez, Ángel López-Urrutia, and Juan José del Coz. "Validation methods for plankton image classification systems." Limnology and Oceanography: Methods 15, no. 3 (2016): 221–37. http://dx.doi.org/10.1002/lom3.10151.

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5

Ellen, Jeffrey S., Casey A. Graff, and Mark D. Ohman. "Improving plankton image classification using context metadata." Limnology and Oceanography: Methods 17, no. 8 (2019): 439–61. http://dx.doi.org/10.1002/lom3.10324.

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6

Cheng, Xuemin, Yong Ren, Kaichang Cheng, Jie Cao, and Qun Hao. "Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye." Sensors 20, no. 9 (2020): 2592. http://dx.doi.org/10.3390/s20092592.

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In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the method of recognition and classification for plankton images is discussed. The optimized classification and recognition networks are constructed. They are available for in situ plankton images, exploiting the advantages of both coordinate systems in the network training process. Fusing the two types of vectors and using them as the input for conventiona

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7

Zhao, Feng, Feng Lin, and Hock Soon Seah. "Binary SIPPER plankton image classification using random subspace." Neurocomputing 73, no. 10-12 (2010): 1853–60. http://dx.doi.org/10.1016/j.neucom.2009.12.033.

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8

Faillettaz, Robin, Marc Picheral, Jessica Y. Luo, Cédric Guigand, Robert K. Cowen, and Jean-Olivier Irisson. "Imperfect automatic image classification successfully describes plankton distribution patterns." Methods in Oceanography 15-16 (April 2016): 60–77. http://dx.doi.org/10.1016/j.mio.2016.04.003.

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9

Li, Xiu, Rujiao Long, Jiangpeng Yan, Kun Jin, and Jihae Lee. "TANet: A Tiny Plankton Classification Network for Mobile Devices." Mobile Information Systems 2019 (April 3, 2019): 1–8. http://dx.doi.org/10.1155/2019/6536925.

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This paper is devoted to a lightweight convolutional neural network based on the attention mechanism called the tiny attention network (TANet). The TANet consists of three main parts termed as a reduction module, self-attention operation, and group convolution. The reduction module alleviates information loss caused by the pooling operation. The new parameter-free self-attention operation makes the model to focus on learning important parts of images. The group convolution achieves model compression and multibranch fusion. Using the main parts, the proposed network enables efficient plankton c

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10

Schröder, Simon-Martin, Rainer Kiko, and Reinhard Koch. "MorphoCluster: Efficient Annotation of Plankton Images by Clustering." Sensors 20, no. 11 (2020): 3060. http://dx.doi.org/10.3390/s20113060.

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In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increas

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11

Plonus, Rene‐Marcel, Jan Conradt, André Harmer, Silke Janßen, and Jens Floeter. "Automatic plankton image classification—Can capsules and filters help cope with data set shift?" Limnology and Oceanography: Methods 19, no. 3 (2021): 176–95. http://dx.doi.org/10.1002/lom3.10413.

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12

González, Pablo, Alberto Castaño, Emily E. Peacock, Jorge Díez, Juan José Del Coz, and Heidi M. Sosik. "Automatic plankton quantification using deep features." Journal of Plankton Research 41, no. 4 (2019): 449–63. http://dx.doi.org/10.1093/plankt/fbz023.

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Abstract The study of marine plankton data is vital to monitor the health of the world’s oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with hand-designed local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision c

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13

Axler, KE, S. Sponaugle, C. Briseño-Avena, et al. "Fine-scale larval fish distributions and predator-prey dynamics in a coastal river-dominated ecosystem." Marine Ecology Progress Series 650 (September 17, 2020): 37–61. http://dx.doi.org/10.3354/meps13397.

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River plumes discharging into continental shelf waters have the potential to influence the distributions, predator-prey relationships, and thus survival of nearshore marine fish larvae, but few studies have been able to characterize the plume environment at sufficiently fine scales to resolve the underlying mechanisms. We used a high-resolution plankton imaging system and a sparse convolutional neural network to automate image classification of larval fishes, their planktonic prey (calanoid copepods), and gelatinous planktonic predators (ctenophores, hydromedusae, and siphonophores) over broad

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14

Campbell, R. W., P. L. Roberts, and J. Jaffe. "The Prince William Sound Plankton Camera: a profiling in situ observatory of plankton and particulates." ICES Journal of Marine Science 77, no. 4 (2020): 1440–55. http://dx.doi.org/10.1093/icesjms/fsaa029.

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Abstract A novel plankton imager was developed and deployed aboard a profiling mooring in Prince William Sound in 2016–2018. The imager consisted of a 12-MP camera and a 0.137× telecentric lens, along with darkfield illumination produced by an in-line ring/condenser lens system. Just under 2.5 × 106 images were collected during 3 years of deployments. A subset of almost 2 × 104 images was manually identified into 43 unique classes, and a hybrid convolutional neural network classifier was developed and trained to identify the images. Classification accuracy varied among the different classes, a

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15

Marchant, Ross, Martin Tetard, Adnya Pratiwi, Michael Adebayo, and Thibault de Garidel-Thoron. "Automated analysis of foraminifera fossil records by image classification using a convolutional neural network." Journal of Micropalaeontology 39, no. 2 (2020): 183–202. http://dx.doi.org/10.5194/jm-39-183-2020.

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Abstract. Manual identification of foraminiferal morphospecies or morphotypes under stereo microscopes is time consuming for micropalaeontologists and not possible for nonspecialists. Therefore, a long-term goal has been to automate this process to improve its efficiency and repeatability. Recent advances in computation hardware have seen deep convolutional neural networks emerge as the state-of-the-art technique for image-based automated classification. Here, we describe a method for classifying large foraminifera image sets using convolutional neural networks. Construction of the classifier

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16

Pereira, G. C., A. R. Figueiredo, and N. F. F. Ebecken. "Using in situ flow cytometry images of ciliates and dinoflagellates for aquatic system monitoring." Brazilian Journal of Biology 78, no. 2 (2017): 240–47. http://dx.doi.org/10.1590/1519-6984.05016.

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Abstract Short-period variability in plankton communities is poorly documented, especially for variations occurring in specific groups in the assemblage because traditional analysis is laborious and time-consuming. Moreover, it does not allow the high sampling frequency required for decision making. To overcome this limitation, we tested the submersible CytoSub flow cytometer. This device was anchored at a distance of approximately 10 metres from the low tide line at a depth of 1.5 metres for 12 hours to monitor the plankton at a site in the biological reserve of Barra da Tijuca beach, Rio de

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17

Albertano, Patrizia, Daniela Di Somma, and Enrico Capucci. "Cyanobacterial picoplankton from the Central Baltic Sea: cell size classification by image-analyzed fluorescence microscopy." Journal of Plankton Research 19, no. 10 (1997): 1405–16. http://dx.doi.org/10.1093/plankt/19.10.1405.

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18

Johansen, Thomas Haugland, and Steffen Aagaard Sørensen. "Towards detection and classification of microscopic foraminifera using transfer learning." Proceedings of the Northern Lights Deep Learning Workshop 1 (February 6, 2020): 6. http://dx.doi.org/10.7557/18.5144.

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Foraminifera are single-celled marine organisms, which may have a planktic or benthic lifestyle. During their life cycle they construct shells consisting of one or more chambers, and these shells remain as fossils in marine sediments. Classifying and counting these fossils have become an important tool in e.g. oceanography and climatology.Currently the process of identifying and counting microfossils is performed manually using a microscope and is very time consuming. Developing methods to automate this process is therefore considered important across a range of research fields.The first steps

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19

Tapics, Tara, Irene Gregory-Eaves, and Yannick Huot. "The private life of Cystodinium: in situ observation of its attachments and population dynamics." Journal of Plankton Research 43, no. 3 (2021): 492–96. http://dx.doi.org/10.1093/plankt/fbab025.

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Abstract Phytoplankton images were collected using an Imaging Flow Cytobot moored in the mesotrophic lake Lac Montjoie (Quebec, Canada). Cystodinium—an unusual dinoflagellate genus—was found during manual classification of the images into taxonomic groups while building an automated classifier. Cystodinium’s particularity is that while it can take a typical motile dinoflagellate form, it is thought to exist primarily as an immotile photosynthetically competent parasitic cyst in the shape of a crescent moon. Observations presented here are of this immotile lunate cyst. Manually classified image

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20

Hrycik, Allison R., Angela Shambaugh, and Jason D. Stockwell. "Comparison of FlowCAM and microscope biovolume measurements for a diverse freshwater phytoplankton community." Journal of Plankton Research 41, no. 6 (2019): 849–64. http://dx.doi.org/10.1093/plankt/fbz056.

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Abstract FlowCAM combines flow cytometry and imaging to rapidly enumerate, classify and measure particles. The instrument potentially increases processing speed of phytoplankton samples. FlowCAM, however, requires extensive comparison to microscopy before incorporation into monitoring and research. Past studies have compared FlowCAM and microscopy results for mostly marine rather than freshwater phytoplankton communities. We compared phytoplankton biovolume, density and taxonomic classifications between FlowCAM and microscopy for 113 samples from Lake Champlain, USA—a large freshwater system w

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21

MacNeil, Liam, Sergey Missan, Junliang Luo, Thomas Trappenberg, and Julie LaRoche. "Plankton classification with high-throughput submersible holographic microscopy and transfer learning." BMC Ecology and Evolution 21, no. 1 (2021). http://dx.doi.org/10.1186/s12862-021-01839-0.

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Abstract Background Plankton are foundational to marine food webs and an important feature for characterizing ocean health. Recent developments in quantitative imaging devices provide in-flow high-throughput sampling from bulk volumes—opening new ecological challenges exploring microbial eukaryotic variation and diversity, alongside technical hurdles to automate classification from large datasets. However, a limited number of deployable imaging instruments have been coupled with the most prominent classification algorithms—effectively limiting the extraction of curated observations from field

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22

Zheng, Haiyong, Ruchen Wang, Zhibin Yu, Nan Wang, Zhaorui Gu, and Bing Zheng. "Automatic plankton image classification combining multiple view features via multiple kernel learning." BMC Bioinformatics 18, S16 (2017). http://dx.doi.org/10.1186/s12859-017-1954-8.

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23

Irisson, Jean-Olivier, Sakina-Dorothée Ayata, Dhugal J. Lindsay, Lee Karp-Boss, and Lars Stemmann. "Machine Learning for the Study of Plankton and Marine Snow from Images." Annual Review of Marine Science 14, no. 1 (2021). http://dx.doi.org/10.1146/annurev-marine-041921-013023.

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Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit this wealth of information, machine learning is necessary to automate tasks such as taxonomic classification. Through a review of the literature, we highlight the progress of those machine classifiers and what they can and still cannot be trusted for. Several examples showcase how the combination of quantitative imaging with machine learning has bro

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24

Yunandar, Dini, HEFNI EFFENDI, WIDIATMAKA, and YUDI SETIAWAN. "Plankton biodiversity in various typologies of inundation in Paminggir peatland, South Kalimantan, Indonesia on dry season." Biodiversitas Journal of Biological Diversity 21, no. 3 (2020). http://dx.doi.org/10.13057/biodiv/d210322.

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Abstract. Yunandar, Effendi H, Widiatmaka, Setiawan Y. 2020. Plankton biodiversity in various typologies of inundation in Paminggir peatland, South Kalimantan, Indonesia on dry season. Biodiversitas 21: 1012-1019. The aim of the study was to analyze the typology of inundation areas and plankton biodiversity in Paminggir peatland, South Borneo, Indonesia. Typology of inundation was determined by image processing and spatial analysis using supervised classification method from Landsat 1994, 2014, 2019. Plankton biodiversity was determined using purposive sampling in detected inundation from spat

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25

Martin-Cabrera, Patricia, Fabien Lombard, Jean-Olivier Irisson, et al. "Coordinating Efforts to Define Marine Plankton Imagery Data and Metadata Best Practices and Standards." Biodiversity Information Science and Standards 4 (September 29, 2020). http://dx.doi.org/10.3897/biss.4.58932.

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“Imagery data” can be referred as qualitative and quantitative information from a collection of images. Imaging systems are used more and more frequently in the marine domain to generate huge amounts of imagery data. For example, automatic image classification is used to determine the abundance, size and biomass of plankton communities. In addition, the recent advances of imaging sensors and the growing datasets, highlight the importance of the management and storage capacity of these data. Thus, establishing data standards, optimized data flows and quality control procedures will promote the

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26

Nayak, Aditya R., Ed Malkiel, Malcolm N. McFarland, Michael S. Twardowski, and James M. Sullivan. "A Review of Holography in the Aquatic Sciences: In situ Characterization of Particles, Plankton, and Small Scale Biophysical Interactions." Frontiers in Marine Science 7 (January 22, 2021). http://dx.doi.org/10.3389/fmars.2020.572147.

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The characterization of particle and plankton populations, as well as microscale biophysical interactions, is critical to several important research areas in oceanography and limnology. A growing number of aquatic researchers are turning to holography as a tool of choice to quantify particle fields in diverse environments, including but not limited to, studies on particle orientation, thin layers, phytoplankton blooms, and zooplankton distributions and behavior. Holography provides a non-intrusive, free-stream approach to imaging and characterizing aquatic particles, organisms, and behavior in

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27

Kaskes, Pim, Sietze J. de Graaff, Jean-Guillaume Feignon, et al. "Formation of the crater suevite sequence from the Chicxulub peak ring: A petrographic, geochemical, and sedimentological characterization." GSA Bulletin, July 9, 2021. http://dx.doi.org/10.1130/b36020.1.

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This study presents a new classification of a ∼100-m-thick crater suevite sequence in the recent International Ocean Discovery Program (IODP)-International Continental Scientific Drilling Program (ICDP) Expedition 364 Hole M0077A drill core to better understand the formation of suevite on top of the Chicxulub peak ring. We provide an extensive data set for this succession that consists of whole-rock major and trace element compositional data (n = 212) and petrographic data supported by digital image analysis. The suevite sequence is subdivided into three units that are distinct in their petrog

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