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Relevant bibliographies by topics / Plankton images

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Journal articles

Academic literature on the topic 'Plankton images'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Plankton images"

1

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 (2021): 012025. http://dx.doi.org/10.1088/1755-1315/944/1/012025.

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Abstract Plankton are free-floating organisms that live, grow, and move along with the ocean currents. This free-floating organism plays important roles as primary producers, they serve as a link to energy transfer, and a factor that regulates the biogeochemical cycles. Indonesia, with almost 60% of its territory covered by the ocean, harbours a wide variety of planktonic species. However, one of the issues within usual planktonic studies is the lack of a fast and accurate method for identifying and classifying the plankton type. Thus, the computer vision methods on microscopic images were pro

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2

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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3

Shahani, Kamran, Hong Song, Syed Raza Mehdi, et al. "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 (2021): 170–78. http://dx.doi.org/10.1007/s11804-020-00185-9.

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AbstractMonitoring the ecology and physiology of corals, sediments, planktons, and microplastic at a suitable spatial resolution is of great importance in oceanic scientific research. To meet this requirement, an underwater microscope with an electrically controlled variable lens was designed and tested. The captured microscopic images of corals, sediments, planktons, and microplastic revealed their physical, biological, and morphological characteristics. Further studies of the images also revealed the growth, degradation, and bleaching patterns of corals; the presence of plankton communities;

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4

Karmini, Mimin, and H. Yuniarto. "BIOSTRATIGRAFI FORAMINIFERA KUARTER PADA BOR INTI MD 982152 DAN 982155 DARI SAMUDRA HINDIA." JURNAL GEOLOGI KELAUTAN 11, no. 2 (2016): 55. http://dx.doi.org/10.32693/jgk.11.2.2013.231.

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Dari bor inti pada EKSPEDISI IMAGES, di Samudra Hindia, telah diteliti sebanyak 21 percontoh sedimen dari lokasi MD 982152, dan 29 buah dari lokasi MD 982155 untuk kepentingan biostratigrafi berdasarkan analisis foraminifera plankton dalam interval 1,5 meter.  Pada kedua penampang bor inti tersebut hanya dijumpai satu zona foraminifera plankton Kuarter, yaitu Zona Globorotalia truncatulinoides. Untuk MD 982152, zona ini bisa dibagi ke dalam dua subzona, yakni Subzona-subzona Globorotalia crassaformis hessi dan Globigerinella calida, sedangkan untuk MD 982155, zona tersebut bisa dibagi la

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5

Luo, T., K. Kramer, D. B. Goldgof, et al. "Recognizing Plankton Images From the Shadow Image Particle Profiling Evaluation Recorder." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 4 (2004): 1753–62. http://dx.doi.org/10.1109/tsmcb.2004.830340.

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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

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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Ohman, Mark D. "A sea of tentacles: optically discernible traits resolved from planktonic organisms in situ." ICES Journal of Marine Science 76, no. 7 (2019): 1959–72. http://dx.doi.org/10.1093/icesjms/fsz184.

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Abstract Trait-based simplifications of plankton community structure require accurate assessment of trait values as expressed in situ. Yet planktonic organisms live suspended in a fluid medium and often bear elongate appendages, delicate feeding structures, and mucous houses that are badly damaged upon capture or removal from the fluid environment. Fixatives further distort organisms. In situ imaging of zooplankton from a fully autonomous Zooglider reveals a suite of trait characteristics that often differ markedly from those inferred from conventionally sampled plankton. In situ images show f

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9

Mcnair, 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 (2021): 103–12. http://dx.doi.org/10.1093/plankt/fbab014.

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Abstract Phytoplankton biomass is routinely estimated using relationships between cell volume and carbon (C) and nitrogen (N) content that have been defined using diverse plankton that span orders of magnitude in size. Notably, volume has traditionally been estimated with geometric approximations of cell shape using cell dimensions from planar two-dimensional (2D) images, which requires assumptions about the third, depth dimension. Given advances in image processing, we examined how cell volumes determined from three-dimensional (3D), confocal images affected established relationships between

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10

Luo, T., K. Kramer, D. B. Goldgof, et al. "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 (2004): 2423. http://dx.doi.org/10.1109/tsmcb.2004.837353.

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