Academic literature on the topic 'Plankton images'
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Journal articles on the topic "Plankton images"
Prakasa, E., A. Rachman, D. R. Noerdjito, and R. Wardoyo. "Development of segmentation algorithm for determining planktonic objects from microscopic images." IOP Conference Series: Earth and Environmental Science 944, no. 1 (December 1, 2021): 012025. http://dx.doi.org/10.1088/1755-1315/944/1/012025.
Full textCampbell, 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 (March 24, 2020): 1440–55. http://dx.doi.org/10.1093/icesjms/fsaa029.
Full textShahani, Kamran, Hong Song, Syed Raza Mehdi, Awakash Sharma, Ghulam Tunio, Junaidullah Qureshi, Noor Kalhoro, and Nooruddin Khaskheli. "Design and Testing of an Underwater Microscope with Variable Objective Lens for the Study of Benthic Communities." Journal of Marine Science and Application 20, no. 1 (March 2021): 170–78. http://dx.doi.org/10.1007/s11804-020-00185-9.
Full textKarmini, Mimin, and H. Yuniarto. "BIOSTRATIGRAFI FORAMINIFERA KUARTER PADA BOR INTI MD 982152 DAN 982155 DARI SAMUDRA HINDIA." JURNAL GEOLOGI KELAUTAN 11, no. 2 (February 16, 2016): 55. http://dx.doi.org/10.32693/jgk.11.2.2013.231.
Full textLuo, T., K. Kramer, D. B. Goldgof, L. O. Hall, S. Samson, A. Remsen, and T. Hopkins. "Recognizing Plankton Images From the Shadow Image Particle Profiling Evaluation Recorder." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 4 (August 2004): 1753–62. http://dx.doi.org/10.1109/tsmcb.2004.830340.
Full textCheng, 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 (May 2, 2020): 2592. http://dx.doi.org/10.3390/s20092592.
Full textSchröder, Simon-Martin, Rainer Kiko, and Reinhard Koch. "MorphoCluster: Efficient Annotation of Plankton Images by Clustering." Sensors 20, no. 11 (May 28, 2020): 3060. http://dx.doi.org/10.3390/s20113060.
Full textOhman, Mark D. "A sea of tentacles: optically discernible traits resolved from planktonic organisms in situ." ICES Journal of Marine Science 76, no. 7 (August 3, 2019): 1959–72. http://dx.doi.org/10.1093/icesjms/fsz184.
Full textMcnair, Heather, Courtney Nicole Hammond, and Susanne Menden-Deuer. "Phytoplankton carbon and nitrogen biomass estimates are robust to volume measurement method and growth environment." Journal of Plankton Research 43, no. 2 (March 2021): 103–12. http://dx.doi.org/10.1093/plankt/fbab014.
Full textLuo, T., K. Kramer, D. B. Goldgof, L. O. Hall, S. Samson, A. Remsen, and T. Hopkins. "Errata to “Recognizing Plankton Images From the Shadow Image Particle Profiling Evaluation Recorder”." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 6 (December 2004): 2423. http://dx.doi.org/10.1109/tsmcb.2004.837353.
Full textDissertations / Theses on the topic "Plankton images"
Kramer, Kurt A. "Identifying plankton from grayscale silhouette images." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001402.
Full textHu, Qiao Ph D. Massachusetts Institute of Technology. "Application of statistical learning theory to plankton image analysis." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/39206.
Full textIncludes bibliographical references (leaves 155-173).
A fundamental problem in limnology and oceanography is the inability to quickly identify and map distributions of plankton. This thesis addresses the problem by applying statistical machine learning to video images collected by an optical sampler, the Video Plankton Recorder (VPR). The research is focused on development of a real-time automatic plankton recognition system to estimate plankton abundance. The system includes four major components: pattern representation/feature measurement, feature extraction/selection, classification, and abundance estimation. After an extensive study on a traditional learning vector quantization (LVQ) neural network (NN) classifier built on shape-based features and different pattern representation methods, I developed a classification system combined multi-scale cooccurrence matrices feature with support vector machine classifier. This new method outperforms the traditional shape-based-NN classifier method by 12% in classification accuracy. Subsequent plankton abundance estimates are improved in the regions of low relative abundance by more than 50%. Both the NN and SVM classifiers have no rejection metrics. In this thesis, two rejection metrics were developed.
(cont.) One was based on the Euclidean distance in the feature space for NN classifier. The other used dual classifier (NN and SVM) voting as output. Using the dual-classification method alone yields almost as good abundance estimation as human labeling on a test-bed of real world data. However, the distance rejection metric for NN classifier might be more useful when the training samples are not "good" ie, representative of the field data. In summary, this thesis advances the current state-of-the-art plankton recognition system by demonstrating multi-scale texture-based features are more suitable for classifying field-collected images. The system was verified on a very large real-world dataset in systematic way for the first time. The accomplishments include developing a multi-scale occurrence matrices and support vector machine system, a dual-classification system, automatic correction in abundance estimation, and ability to get accurate abundance estimation from real-time automatic classification. The methods developed are generic and are likely to work on range of other image classification applications.
by Qiao Hu.
Ph.D.
Fernandez, Mariela Atausinchi. "Classificação de imagens de plâncton usando múltiplas segmentações." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-29052017-141908/.
Full textPlankton are microscopic organisms that constitute the basis of the food chain of aquatic ecosystems. They have an important role in the carbon cycle as they are responsible for the absorption of carbon in the ocean surfaces. Detecting, estimating and monitoring the distribution of plankton species are important activities for understanding the role of plankton and the consequences of changes in their environment. Part of these type of studies is based on the analysis of water volumes by means of imaging techniques. Due to the large quantity of generated images, computational methods for helping the process of image analysis are in demand. In this work we address the problem of species identification. We follow the conventional pipeline consisting of target detection, segmentation (contour delineation), feature extraction, and classification steps. In the first part of this work we address the problem of choosing an appropriate segmentation algorithm. Since evaluating segmentation results is a subjective and time consuming task, we propose a method to evaluate segmentation algorithms by evaluating the classification results at the end of the pipeline. Experiments with this method showed that distinct segmentation algorithms might be appropriate for identifying species of distinct classes. Therefore, in the second part of this work we propose a classification method that takes into consideration multiple segmentations. Specifically, multiple segmentations are computed and classifiers are trained individually for each segmentation, which are then combined to build the final classifier. Experimental results show that the accuracy obtained with the combined classifier is superior in more than 2% to the accuracy obtained with classifiers using a fixed segmentation. The proposed methods can be useful to build plankton identification systems that are able to quickly adjust to changes in the characteristics of the images.
Bureš, Jaroslav. "Klasifikace obrazů planktonu s proměnlivou velikosti pomocí konvoluční neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417282.
Full textLiu, Zonghua. "A shape-based image classification and identification system for digital holograms of marine particles and plankton." Thesis, University of Aberdeen, 2018. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=238473.
Full textSoviadan, Yawouvi Dodji. "Distribution et fonction du mésozooplancton dans le premier kilomètre de l’océan mondial." Electronic Thesis or Diss., Sorbonne université, 2021. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2021SORUS469.pdf.
Full textMesozooplankton refers to all aquatic animals between 200 µm and 2000 µm that drift with the currents. The variability of mesozooplankton plays a major role in the carbon cycle and global changes through direct and indirect effects. It is distributed throughout the water column from the surface to the abyss. The mesopelagic zone (between 200 and 1000 m depth) is a critical water layer because of the physical and biological processes affecting carbon fluxes that take place there. However, mesopelagic mesozooplankton is rarely studied, due to sampling constraints and the lack of taxonomic knowledge of a community that is still poorly studied. The collection of samples from the Tara Oceans expedition analyzed by imaging at the Laboratoire d'Océanographie de Villefranche sur Mer has allowed the generation of a global mesozooplankton database, from the surface to the lower limit of the mesopelagic zone (1000 m). The combination of taxonomic and morphometric data generated by the imaging technique allows: i) to describe the faunal structure of the mesozooplankton; ii) to study its size structure; and iii) to calculate the physiological rates of crustaceans to estimate their contribution to the carbon budget in the global ocean, from the surface to 1000 m. These data have been augmented with data from the Malaspina cruise, recent Geomar cruises and in-situ imaging data of vertical particles profiles (underwater vision profiler, UVP) from Tara Oceans. This thesis is a first step towards the analysis of descriptor variables and the distribution of mesozooplankton communities in the mesopelagic zone at the global scale, in relation with vertical particles fluxes and hydrological and biogeochemical variables. Our results show that the structure of epipelagic mesozooplankton communities at the global scale depends mainly on temperature, phytoplankton composition, and surface-produced particulate organic matter. In the mesopelagic layer, the main factors structuring the mesozooplankton are surface phytoplankton composition, particulate concentration, temperature and dissolved oxygen concentration. The size structure of the mesozooplankton was studied through the analysis of slopes and shapes of the normalized biomass size spectrum or the normalized biovolume size spectrum (NBSS). Our results show that position in the water column (depth) is a more important factor than the effect of latitude in explaining differences between mesozooplankton communities (relative abundances of taxa, biomass, NBSS). NBSS observed in tropical regions reflect a drastic decrease in mesozooplankton abundance, accompanied by a decrease in their spectral slopes (steeper), while their shapes were less affected. NBSS of large mesozooplankton and particles > 500 µm ESD obtained from two different methods (net collection and imaging by ZooScan, and in situ imaging, UVP, respectively) allowed to directly compare and intercalibrate their NBSS from oligotrophic to eutrophic systems. Results show that nets significantly underestimate fragile organisms such as rhizarians and UVP underestimates copepods, with high variability with latitude and depth. Mesozooplankton NBSS estimated by both instruments are in agreement at locations where copepods dominate, in the temperate and polar oceans. Analysis of tropical crustacean NBSS reveals the existence of five types communities, associated with distinct habitats: surface rich environment, upper mesopelagic rich environment, lower mesopelagic poor environment, oligotrophic mesopelagic and oxygen minimum zones (OMZ) [...]
Roosmawati, Nova. "Long-Term Surface Uplift History of the Active Banda Arc-Continent Collision: Depth and Age Analysis of Foraminifera from Rote and Savu Islands, Indonesia." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd887.pdf.
Full textDave, Palak P. "A Quantitative Analysis of Shape Characteristics of Marine Snow Particles with Interactive Visualization: Validation of Assumptions in Coagulation Models." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7279.
Full textPanaïotis, Thelma. "Distribution du plancton à diverses échelles : apport de l'intelligence artificielle pour l'écologie planctonique." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS155.
Full textAs the basis of oceanic food webs and a key component of the biological carbon pump, planktonic organisms play major roles in the oceans. However, their small-scale distribution − governed by biotic interactions between organisms and interactions with the physico-chemical properties of the water masses in their immediate environment − are poorly described in situ due to the lack of suitable observation tools. New instruments performing high resolution imaging in situ in combination with machine learning algorithms to process the large amount of collected data now allows us to address these scales. The first part of this work focuses on the methodological development of two automated pipelines based on artificial intelligence. These pipelines allowed to efficiently detect planktonic organisms within raw images, and classify them into taxonomical or morphological categories. Then, in a second part, numerical ecology tools have been applied to study plankton distribution at different scales, using three different in situ imaging datasets. First, we investigated the link between plankton community and environmental conditions at the global scale. Then, we resolved plankton and particle distribution across a mesoscale front, and highlighted contrasted periods during the spring bloom. Finally, leveraging high frequency in situ imaging data, we investigated the fine-scale distribution and preferential position of Rhizaria, a group of understudied, fragile protists, some of which are mixotrophic. Overall, these studies demonstrate the effectiveness of in situ imaging combined with artificial intelligence to understand biophysical interactions in plankton and distribution patterns at small-scale
"Binary plankton recognition using random sampling." Thesis, 2006. http://library.cuhk.edu.hk/record=b6074163.
Full textDue to the complexity of plankton recognition problem, it is difficult to pursue a single optimal classifier to meet all the requirements. In this work, instead of developing a single sophisticated classifier, we propose an ensemble learning framework based on the random sampling techniques including random subspace and bagging. In the random subspace method, a set of low-dimensional subspaces are generated by randomly sampling on the feature space, and multiple classifiers constructed from these random subspaces are combined to yield a powerful classifier. In the bagging approach, a number of independent bootstrap replicates are generated by randomly sampling with replacement on the training set. A classifier is trained on each replicate, and the final result is produced by integrating all the classifiers using majority voting. Using random sampling, the constructed classifiers are stable and multiple classifiers cover the entire feature space or the whole training set without losing discriminative information. Thus, good performance can be achieved. Experimental results demonstrate the effectiveness of the random sampling techniques for improving the system performance.
On the other hand, in previous approaches, normally the samples of all the plankton classes are used for a single classifier training. It may be difficult to select one feature space to optimally represent and classify all the patterns. Therefore, the overall accuracy rate may be low. In this work, we propose a pairwise classification framework, in which the complex multi-class plankton recognition problem is transformed into a set of two-class problems. Such a problem decomposition leads to a number of simpler classification problems to be solved, and it provides an approach for independent feature selection for each pair of classes. This is the first time for such a framework introduced in plankton recognition. We achieve nearly perfect classification accuracy on every pairwise classifier with less number of selected features, since it is easier to select an optimal feature vector to discriminate the two-class patterns. The ensemble of these pairwise classifiers will increase the overall performance. A high accuracy rate of 94.49% is obtained from a collection of more than 3000 plankton images, making it comparable with what a trained biologist can achieve by using conventional manual techniques.
Plankton including phytoplankton and zooplankton form the base of the food chain in the ocean and are a fundamental component of marine ecosystem dynamics. The rapid mapping of plankton abundance together with taxonomic and size composition can help the oceanographic researchers understand how climate change and human activities affect marine ecosystems.
Recently the University of South Florida developed the Shadowed Image Particle Profiling and Evaluation Recorder (SIPPER), an underwater video system which can continuously capture the magnified plankton images in the ocean. The SIPPER images differ from those used for most previous research in four aspects: (i) the images are much noisier, (ii) the objects are deformable and often partially occluded, (iii) the images are projection variant, i.e., the images are video records of three-dimensional objects in arbitrary positions and orientations, and (iv) the images are binary thus are lack of texture information. To deal with these difficulties, we implement three most valuable general features (i.e., moment invariants, Fourier descriptors, and granulometries) and propose a set of specific features such as circular projections, boundary smoothness, and object density to form a more complete description of the binary plankton patterns. These features are translation, scale, and rotation invariant. Moreover, they are less sensitive to noise. High-quality features will surely benefit the overall performance of the plankton recognition system.
Since all the features are extracted from the same plankton pattern, they may contain much redundant information and noise as well. Different types of features are incompatible in length and scale and the combined feature vector has a higher dimensionality. To make the best of these features for the binary SIPPER plankton image classification, we propose a two-stage PCA based scheme for feature selection, combination, and normalization. The first-stage PCA is used to compact every long feature vector by removing the redundant information and reduce noise as well, and the second-stage PCA is employed to compact the combined feature vector by eliminating the correlative information among different types of features. In addition, we normalize every component in the combined feature vector to the same scale according to its mean value and variance. In doing so, we reduce the computation time for the later recognition stage, and improve the classification accuracy.
Zhao Feng.
"May 2006."
Adviser: Xiaoou Tang.
Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6666.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2006.
Includes bibliographical references (p. 121-136).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstracts in English and Chinese.
School code: 1307.
Books on the topic "Plankton images"
Torma, Franziska, ed. A Cultural History Of The Sea in the Global Age. Bloomsbury Publishing Plc, 2021. http://dx.doi.org/10.5040/9781474207249.
Full textBook chapters on the topic "Plankton images"
Hirata, Nina S. T., Alexandre Morimitsu, and Antonio Goulart. "Separating Particles from Plankton Images." In Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, 445–59. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37731-0_33.
Full textGuo, Guannan, Qi Lin, Tao Chen, Zhenghui Feng, Zheng Wang, and Jianping Li. "Colorization for in situ Marine Plankton Images." In Lecture Notes in Computer Science, 216–32. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19839-7_13.
Full textBureš, Jaroslav, Tuomas Eerola, Lasse Lensu, Heikki Kälviäinen, and Pavel Zemčík. "Plankton Recognition in Images with Varying Size." In Pattern Recognition. ICPR International Workshops and Challenges, 110–20. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68780-9_11.
Full textTang, Xiaoou, W. Kenneth Stewart, Luc Vincent, He Huang, Marty Marra, Scott M. Gallager, and Cabell S. Davis. "Automatic Plankton Image Recognition." In Artificial Intelligence for Biology and Agriculture, 177–99. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5048-4_9.
Full textPastore, Vito Paolo, Nimrod Megiddo, and Simone Bianco. "An Anomaly Detection Approach for Plankton Species Discovery." In Image Analysis and Processing – ICIAP 2022, 599–609. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06430-2_50.
Full textZelenka, Claudius, and Reinhard Koch. "Single Image Plankton 3D Reconstruction from Extended Depth of Field Shadowgraph." In Pattern Recognition and Information Forensics, 76–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05792-3_8.
Full textBenammar, Nassima, Haithem Kahil, Anas Titah, Facundo M. Calcagno, Amna Abidi, and Mouna Ben Mabrouk. "Improving 3D Plankton Image Classification with C3D2 Architecture and Context Metadata." In Innovations in Bio-Inspired Computing and Applications, 170–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96299-9_17.
Full textMashkov, Oleg, Victoria Kosenko, Nataliia Savina, Yuriy Rozov, Svitlana Radetska, and Mariia Voronenko. "Information Technologies for Environmental Monitoring of Plankton Algae Distribution Based on Satellite Image Data." In Advances in Intelligent Systems and Computing, 434–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26474-1_31.
Full textSieracki, Michael E., and L. Kenneth Webb. "The Application of Image Analysed Fluorescence Microscopy for Characterising Planktonic Bacteria and Protists." In Protozoa and Their Role in Marine Processes, 77–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-73181-5_5.
Full textPhilippe, Grosjean, and Denis Kevin. "Supervised Classification of Images, Applied to Plankton Samples Using R and Zooimage." In Data Mining Applications with R, 331–65. Elsevier, 2014. http://dx.doi.org/10.1016/b978-0-12-411511-8.00013-x.
Full textConference papers on the topic "Plankton images"
Alfano, Paolo Didier, Marco Rando, Marco Letizia, Francesca Odone, Lorenzo Rosasco, and Vito Paolo Pastore. "Efficient Unsupervised Learning for Plankton Images." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956360.
Full textMa, Wenqi, Tao Chen, Zhengwen Zhang, Zhenyu Yang, Chao Dong, Jianping Qiao, and Jianping Li. "Super-resolution for in situ Plankton Images." In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00411.
Full textPu, Yuchun, Zhenghui Feng, Zhonglei Wang, Zhenyu Yang, and Jianping Li. "Anomaly Detection for In situ Marine Plankton Images." In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00409.
Full textCheng, Kaichang, Xuemin Cheng, and Qun Hao. "A Review of Feature Extraction Technologies for Plankton Images." In the 2018 International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3292425.3293462.
Full textGoulart, Antonio Jose Homsi, Alexandre Morimitsu, Renan Jacomassi, Nina Hirata, and Rubens Lopes. "Deep learning and t-SNE projection for plankton images clusterization." In OCEANS 2021: San Diego – Porto. IEEE, 2021. http://dx.doi.org/10.23919/oceans44145.2021.9706043.
Full textSun, Qiantai, Xinwei Wang, Jianan Chen, Liang Sun, Pingshun Lei, Jun He, and Yan Zhou. "Automatic region of interest extraction in underwater plankton darkfield images." In Imaging Detection and Target Recognition, edited by Jiangtao Xu and Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3020055.
Full textZimmerman, Thomas G., Johnny K. Duong, Ziah Dean, Simone Bianco, and Raymond Esquerra. "Evaluating automated reconstruction methods for digital inline holographic images of plankton." In Practical Holography XXXVI: Displays, Materials, and Applications, edited by Hans I. Bjelkhagen and Seung-Hyun Lee. SPIE, 2022. http://dx.doi.org/10.1117/12.2612438.
Full textNavarro, Gabriel, and Javier Ruiz. "Elements of spatial and temporal variability of plankton in the gulf of Cadiz: an analysis based on EOF decomposition of SeaWiFS images." In Remote Sensing, edited by Charles R. Bostater, Jr. and Rosalia Santoleri. SPIE, 2004. http://dx.doi.org/10.1117/12.514031.
Full textBeemer, Ryan D., Alexandre N. Bandini-Maeder, Jeremy Shaw, Ulysse Lebrec, and Mark J. Cassidy. "The Granular Structure of Two Marine Carbonate Sediments." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77087.
Full textMarshall, Lauren, Adam Schroeder, and Brian Trease. "Comparing Fish-Inspired Ram Filters for Collection of Harmful Algae." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88797.
Full textReports on the topic "Plankton images"
Neeley, Aimee, Stace E. Beaulieu, Chris Proctor, Ivona Cetinić, Joe Futrelle, Inia Soto Ramos, Heidi M. Sosik, et al. Standards and practices for reporting plankton and other particle observations from images. Woods Hole Oceanographic Institution, July 2021. http://dx.doi.org/10.1575/1912/27377.
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