Bibliografías temáticas / Plankton images

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Literatura académica sobre el tema "Plankton images"

Autor: Grafiati
Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Plankton images"

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Prakasa, E., A. Rachman, D. R. Noerdjito y R. Wardoyo. "Development of segmentation algorithm for determining planktonic objects from microscopic images". IOP Conference Series: Earth and Environmental Science 944, n.º 1 (1 de diciembre de 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 proposed to deal with the problem. The classification follows two main steps, detecting plankton location and followed by plankton differentiation. The segmentation algorithm is required to limit the determination area. The present study describes the segmentation methods on fifteen plankton types. The U-Net based architecture was implemented to segment the plankton texture from other objects. The segmentation result was also compared with the manual assessment to compute the performance parameters. The accuracy, 0.970±0.025, gives the highest value whereas the smallest value is found in the precision parameter, 0.761±0.156.
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Campbell, R. W., P. L. Roberts y J. Jaffe. "The Prince William Sound Plankton Camera: a profiling in situ observatory of plankton and particulates". ICES Journal of Marine Science 77, n.º 4 (24 de marzo de 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, and applying thresholds to the output of the neural network (interpretable as probabilities or classifier confidence), improved classification accuracy in non-ambiguous groups to between 80% and 100%.
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Shahani, Kamran, Hong Song, Syed Raza Mehdi, Awakash Sharma, Ghulam Tunio, Junaidullah Qureshi, Noor Kalhoro y 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, n.º 1 (marzo de 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; and the types of microplastics. The imaging performance is majorly influenced by the choice of lenses, camera selection, and lighting method. Image dehazing, global saturation masks, and image histograms were used to extract the image features. Fundamental experimental proof was obtained with micro-scale images of corals, sediments, planktons, and microplastic at different magnifications. The designed underwater microscope can provide relevant new insights into the observation and detection of the future conditions of aquatic ecosystems.
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Karmini, Mimin y H. Yuniarto. "BIOSTRATIGRAFI FORAMINIFERA KUARTER PADA BOR INTI MD 982152 DAN 982155 DARI SAMUDRA HINDIA". JURNAL GEOLOGI KELAUTAN 11, n.º 2 (16 de febrero de 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 lagi ke dalam tiga subzona, yakni Subzona-subzona Globorotalia crassaformis hessi Globigerinella calida, dan Beella digitata. Kejadian yang signifikan di kedua penampang itu adalah Datum Pemunculan Pertama dari Globigerinella calida dan Pemunculan Akhir dari Globorotalia crassaformis hessi. Pada MD 982155, dijumpai Pemunculan Pertama dari Beella digitata. Kata kunci: foraminifera plankton, Kuarter, biostratigrafi, Samudra Hindia. From IMAGES Expedition in Indian Ocean, 21 samples from MD 982152, and 29 samples from MD 982155 had been studied for the purpose of biostratigraphy based on planktonic foraminifera within 1,5 meter interval. In both sections, only one Quaternary zone is found, namely Globorotalia truncatulinoides Zone. For MD 982152, that zone can be subdivided into two interval subzones e.g. Globorotalia crassaformis hessi and Globigerinella calida calida. However, in MD 982155 Globorotalia truncatulinoides Zone can be subdivided into three subzones namely, Globorotalia crassaformis hessi, Globigerinella calida calida, and Beella digitata Subzones. The planktonic foraminifera event revealed in both sections are the First Appearance Datum (FAD) of Globigerinella calida calida and the Last Appearance (LAD) of Globorotalia crassaformis hessi. In MD 982155 the FAD of Beella digitata is found. Keywords: planktonic foraminifera, Quaternary, biostratigraphy, Indian Ocean.
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Luo, T., K. Kramer, D. B. Goldgof, L. O. Hall, S. Samson, A. Remsen y T. Hopkins. "Recognizing Plankton Images From the Shadow Image Particle Profiling Evaluation Recorder". IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, n.º 4 (agosto de 2004): 1753–62. http://dx.doi.org/10.1109/tsmcb.2004.830340.

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Cheng, Xuemin, Yong Ren, Kaichang Cheng, Jie Cao y 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, n.º 9 (2 de mayo de 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 conventional machine learning models for classification, support vector machines (SVMs) are selected as the classifiers to combine these two features of vectors, coming from different image coordinate descriptions. The accuracy of the proposed model was markedly higher than those of the initial classical convolutional neural networks when using the in situ plankton image data, with the increases in classification accuracy and recall rate being 5.3% and 5.1% respectively. In addition, the proposed training method can improve the classification performance considerably when used on the public CIFAR-10 dataset.
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Schröder, Simon-Martin, Rainer Kiko y Reinhard Koch. "MorphoCluster: Efficient Annotation of Plankton Images by Clustering". Sensors 20, n.º 11 (28 de mayo de 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 increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection.
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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, n.º 7 (3 de agosto de 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 fragile feeding appendages in natural hunting postures, including reticulate networks of rhizopods, feeding tentacles of cnidarians, and tentilla of ctenophores; defensive spines and setae of copepods; intact mucous houses of appendicularians; and other structures that are not discernible in conventionally collected zooplankton. Postures characteristic of dormant copepods can be identified and the presence of egg sacs detected. Intact, elongate diatom chains that are much longer than measured in sampled specimens are resolvable in situ. The ability to image marine snow, as well as small-scale fluid deformations, reveals micro-habitat structure that may alter organismal behaviour. Trait-based representations of planktonic organisms in biogeochemical cycles need to consider naturally occurring traits expressed by freely suspended planktonic organisms in situ.
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Mcnair, Heather, Courtney Nicole Hammond y Susanne Menden-Deuer. "Phytoplankton carbon and nitrogen biomass estimates are robust to volume measurement method and growth environment". Journal of Plankton Research 43, n.º 2 (marzo de 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 phytoplankton cell volume and C and N content. Additionally, we determined that growth conditions could result in 30–40% variation in cellular N and C. 3D phytoplankton cell volume measurements were on average 15% greater than the geometric approximations from 2D images. Volume method variation was minimal compared to both intraspecific variation in volumes (~30%) and the 50-fold variation in elemental density among species. Consequently, C:vol and N:vol relationships were unaltered by volume measurement method and growth environment. Recent advances in instrumentation, including those for at sea and autonomous applications can be used to estimate plankton biomass directly. Going forward, we recommend instrumentation that permits species identification alongside size and shape characteristics for plankton biomass estimates.
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Luo, T., K. Kramer, D. B. Goldgof, L. O. Hall, S. Samson, A. Remsen y 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, n.º 6 (diciembre de 2004): 2423. http://dx.doi.org/10.1109/tsmcb.2004.837353.

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Tesis sobre el tema "Plankton images"

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Kramer, Kurt A. "Identifying plankton from grayscale silhouette images". [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001402.

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Hu, 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.

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Thesis (Ph. D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2006.
Includes 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.
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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/.

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Plâncton são organismos microscópicos que constituem a base da cadeia alimentar de ecossistemas aquáticos. Eles têm importante papel no ciclo do carbono pois são os responsáveis pela absorção do carbono na superfície dos oceanos. Detectar, estimar e monitorar a distribuição das diferentes espécies são atividades importantes para se compreender o papel do plâncton e as consequências decorrentes de alterações em seu ambiente. Parte dos estudos deste tipo é baseada no uso de técnicas de imageamento de volumes de água. Devido à grande quantidade de imagens que são geradas, métodos computacionais para auxiliar no processo de análise das imagens estão sob demanda. Neste trabalho abordamos o problema de identificação da espécie. Adotamos o pipeline convencional que consiste dos passos de detecção de alvo, segmentação (delineação de contorno), extração de características, e classificação. Na primeira parte deste trabalho abordamos o problema de escolha de um algoritmo de segmentação adequado. Uma vez que a avaliação de resultados de segmentação é subjetiva e demorada, propomos um método para avaliar algoritmos de segmentação por meio da avaliação da classificação no final do pipeline. Experimentos com esse método mostraram que algoritmos de segmentação distintos podem ser adequados para a identificação de espécies de classes distintas. Portanto, na segunda parte do trabalho propomos um método de classificação que leva em consideração múltiplas segmentações. Especificamente, múltiplas segmentações são calculadas e classificadores são treinados individualmente para cada segmentação, os quais são então combinados para construir o classificador final. Resultados experimentais mostram que a acurácia obtida com a combinação de classificadores é superior em mais de 2% à acurácia obtida com classificadores usando uma segmentação fixa. Os métodos propostos podem ser úteis para a construção de sistemas de identificação de plâncton que sejam capazes de se ajustar rapidamente às mudanças nas características das imagens.
Plankton 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.
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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.

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Tato práce pojednává o technikách automatické analýzy obrazu založené na konvolučních neuronových sítích (CNN), zaměřených na klasifikaci planktonu. V oblasti studování planktonu panuje velká diverzita v jeho tvarech a velikostech. Kvůli tomuto bývá klasifikace pomocí CNN náročná, jelikož CNN typicky požadují definovanou velikost vstupu. Běžné metody využívají škálování obrazu do jednotné velikosti. Avšak kvůli tomuto jsou ztraceny drobné detaily potřebné ke správné klasifikaci. Cílem práce bylo navrhnout a implementovat CNN klasifikátor obrazových dat planktonu a prozkoumat metody, které jsou zaměřené na problematiku různorodých velikostí obrázků. Metody, jako jsou patch cropping, využití spatial pyramid pooling vrstvy, zahrnutí metadat a sestavení multi-stream modelu jsou vyhodnoceny na náročném datasetu obrázků fytoplanktonu. Takto bylo dosaženo zlepšení o 1.0 bodů pro InceptionV3 architekturu s výslednou úspěšností 96.2 %. Hlavním přínosem této práce je vylepšení CNN klasifikátorů planktonu díky úspěšné aplikaci těchto metod.
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Liu, 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.

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The objective of this project is to develop a shape-based image analysis system, which allows classification and identification of holographic images of marine particles and plankton recorded by an underwater digital holographic camera. In order to achieve this goal, the first step is to extract shape regions of objects from images and to describe the regions by polygonal boundaries. After extraction of the polygonal boundary curve of an object, affine-invariant curve normalisation is implemented on the curve to reduce the influence of object shape deformations on object identification and classification. Six numeric features are then selected to describe shape properties of an object. Before these six shape features are used as a numeric interpretation of an object for image analysis, some processing of them is necessary, consisting of selecting the number of items in each feature and rescaling the selected feature vectors. Afterwards, Gaussian rescaling is adopted to rescale the feature data. Lastly, a shape-based image classification and identification system is built. The system contains two components: semi-automatic image classification (imCLASS) and automatic image identification (imIDENT). In imCLASS, an image retrieval method based on the support vector machine with a feedback mechanism has been developed. The function of imCLASS is to classify given images into different folders with the corresponding labels from the user. These labelled folders can be used to train the artificial neural network in imIDENT. A set of analyses of effects of the proposed methods in the process chain on image analysis are carried out. The whole performance of the system for classifying and identifying marine particles and plankton is also evaluated in terms of the time-cost and accuracy performance. In the end, some main conclusions are listed. The areas of weakness of the system are also highlighted for future work.
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Soviadan, 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.

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Le mésozooplancton désigne l'ensemble des animaux aquatiques compris entre 200 µm et 2000 µm, qui ne peuvent pas s’affranchir des courants. La variabilité du mésozooplancton joue un rôle majeur dans le cycle du carbone et les changements globaux à travers des effets directs et indirects. Il se distribue sur toute la colonne d’eau depuis la surface jusqu’aux abysses. La zone mésopélagique (entre 200 et 1000 m de profondeur) constitue une couche d’eau critique en raison des processus physiques et biologiques affectant les flux de carbone qui s’y déroulent. Toutefois, le mésozooplancton mésopélagique est rarement étudié, en raison des contraintes d'échantillonnage et de la méconnaissance taxonomique d’une communauté encore peu étudiée. La collection d’échantillons de l'expédition Tara Océans analysée par imagerie au Laboratoire d'Océanographie de Villefranche sur Mer a permis de générer une base de données de mésozooplancton d’emprise globale, de la surface jusqu’à la limite inférieure de la zone mésopélagique (1000 m). La combinaison des données taxonomiques et morphométriques générées par l’imagerie permet: i) de décrire la structure faunistique du mésozooplancton ; ii) d'étudier sa structure en taille; et iii) de calculer les taux physiologiques des crustacés pour estimer leur contribution au budget de carbone dans l'océan global, de la surface jusqu’à 1000 m. Ces données ont été augmentées des données de la campagne Malaspina, des récentes campagnes Geomar et des données d’imagerie in-situ de profils verticaux de particules (profileur de vision sous-marine, UVP) de Tara Océans. Cette thèse est une première étape vers l'analyse des variables descriptrices et la distribution des communautés de mésozooplancton dans la zone mésopélagique à l’échelle globale, en relation avec les flux verticaux de particules et les variables hydrologiques et biogéochimiques. Nos résultats montrent que la structure des communautés mésozooplanctoniques épipélagiques à l'échelle globale dépend essentiellement de la température, de la composition du phytoplancton, et de la matière organique particulaire produite en surface. Dans la couche mésopélagique, les principaux facteurs structurant le mésozooplancton sont la composition du phytoplancton de surface, la concentration en particules, la température et la concentration en oxygène dissous. La structure en taille du mésozooplancton a été étudiée à travers l’analyse des pentes et des formes des spectres de taille en biomasse normalisée ou en biovolume normalisé (NBSS). Nos résultats montrent que la position dans la colonne d’eau (profondeur) est un facteur plus important que l’effet de la latitude pour expliquer les différences entre communautés de mésozooplancton (abondances relatives des taxons, biomasse, NBSS). Les NBSS observés dans les régions tropicales sont le reflet d’une diminution drastique de l'abondance du mésozooplancton, s’accompagnant d’une diminution de leurs pentes spectrales (plus pentues), tandis que leurs formes changent peu. Les NBSS du grand mésozooplancton et des particules > 500 µm ESD obtenus à partir de deux méthodes différentes (collecte au filet et imagerie par ZooScan, et imagerie in situ, UVP, respectivement) ont permis de comparer et intercalibrer directement leurs NBSS, des systèmes oligotrophes aux systèmes eutrophes. Les résultats montrent que les filets sous-échantillonnent significativement les organismes fragiles tels que les rhizaires et que l’UVP sous-échantillonne les copépodes, avec une forte variabilité en fonction de la latitude et de la profondeur. Les NBSS du mésozooplancton estimés par les deux instruments concordent aux endroits où les copépodes dominent, dans les océans tempérés et polaires [...]
Mesozooplankton 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) [...]
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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.

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Dave, 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.

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The Deepwater Horizon oil spill that started on April 20, 2010, in the Gulf of Mexico was the largest marine oil spill in the history of the petroleum industry. There was an unexpected and prolonged sedimentation event of oil-associated marine snow to the seafloor due to the oil spill. The sedimentation event occurred because of the coagulation process among oil associated marine particles. Marine scientists are developing models for the coagulation process of marine particles and oil, in order to estimate the amount of oil that may reach the seafloor along with marine particles. These models, used certain assumptions regarding the shape and the texture parameters of marine particles. Such assumptions may not be based on accurate information or may vary during and after the oil spill. The work performed here provided a quantitative analysis of the assumptions used in modeling the coagulation process of marine particles. It also investigated the changes in model parameters (shape and texture) during and after the Deepwater Horizon oil spill in different seasons (spring and summer). An Interactive Visualization Application was developed for data exploration and visual analysis of the trends in these parameters. An Interactive Statistical Analysis Application was developed to create a statistical summary of these parameter values.
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Panaïotis, 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.

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En tant que base des réseaux trophiques océaniques et élément clé de la pompe à carbone biologique, les organismes planctoniques jouent un rôle majeur dans les océans. Cependant, leur distribution à petite échelle, régie par les interactions biotiques entre organismes et les interactions avec les propriétés physico-chimiques des masses d'eau de leur environnement immédiat, est mal décrite in situ, en raison du manque d'outils d'observation adaptés. De nouveaux instruments d'imagerie in situ à haute résolution, combinés à des algorithmes d'apprentissage automatique pour traiter la grande quantité de données collectées, nous permettent aujourd'hui d'aborder ces échelles. La première partie de ce travail se concentre sur le développement méthodologique de deux pipelines automatisés basés sur l'intelligence artificielle. Ces pipelines ont permis de détecter efficacement les organismes planctoniques au sein des images brutes, et de les classer en catégories taxonomiques ou morphologiques. Dans une deuxième partie, des outils d'écologie numérique ont été appliqués pour étudier la distribution du plancton à différentes échelles, en utilisant trois jeux de données d'imagerie in situ. Tout d'abord, nous avons mis en évidence un lien entre les communautés planctoniques et les conditions environnementales à l'échelle globale. Ensuite, nous avons décrit la distribution du plancton et des particules à travers un front de méso-échelle, et mis en évidence des périodes contrastées pendant le bloom de printemps. Enfin, grâce aux données d'imagerie in situ à haute fréquence, nous avons étudié la distribution à fine échelle et la position préférentielle d’organismes appartement au groupe des Rhizaria, des protistes fragiles et peu étudiés, dont certains sont mixotrophes. Dans l’ensemble, ce travail démontre l'efficacité de l'imagerie in situ combinée à des approches d’intelligence artificielle pour comprendre les interactions biophysiques dans le plancton et les conséquences sur sa distribution à petite échelle
As 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
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"Binary plankton recognition using random sampling". Thesis, 2006. http://library.cuhk.edu.hk/record=b6074163.

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Among the these proposed methods (i.e., random subspace, bagging, and pairwise classification), the pairwise classification method produces the highest accuracy at the expense of more computation time for training classifiers. The random subspace method and bagging approach have similar performance. To recognize a testing plankton pattern, the computational costs of the these methods are alike.
Due 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.
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Libros sobre el tema "Plankton images"

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Torma, Franziska, ed. A Cultural History Of The Sea in the Global Age. Bloomsbury Publishing Plc, 2021. http://dx.doi.org/10.5040/9781474207249.

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In 1972 an image became an icon: ‘Blue Marble’, a photograph of the Earth as seen from outer space. The picture features prominently the globe’s water-covered surface. The ocean connects nature and culture in the modern world. Within the time-span of 100 years, the sea changed its cultural meaning, from a dangerous place to an endangered environment. This volume traces diverse processes of oceanic transformation in the Anthropocene: it follows scientists, seafarers, diplomats and filmmakers from ship-decks to the arenas of political decision making on land. The essays lead from underwater dumping grounds to islands in the south pacific. Tiny organisms like plankton and charismatic megafauna like whales accompanied the human voyages. The presence of the animals challenges common notions of human culture. The global age has to take nonhuman agents into account to fully understand the cultural history of the seas.
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Capítulos de libros sobre el tema "Plankton images"

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Hirata, Nina S. T., Alexandre Morimitsu y Antonio Goulart. "Separating Particles from Plankton Images". En 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.

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Guo, Guannan, Qi Lin, Tao Chen, Zhenghui Feng, Zheng Wang y Jianping Li. "Colorization for in situ Marine Plankton Images". En Lecture Notes in Computer Science, 216–32. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19839-7_13.

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Bureš, Jaroslav, Tuomas Eerola, Lasse Lensu, Heikki Kälviäinen y Pavel Zemčík. "Plankton Recognition in Images with Varying Size". En 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.

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Tang, Xiaoou, W. Kenneth Stewart, Luc Vincent, He Huang, Marty Marra, Scott M. Gallager y Cabell S. Davis. "Automatic Plankton Image Recognition". En Artificial Intelligence for Biology and Agriculture, 177–99. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5048-4_9.

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Pastore, Vito Paolo, Nimrod Megiddo y Simone Bianco. "An Anomaly Detection Approach for Plankton Species Discovery". En 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.

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Zelenka, Claudius y Reinhard Koch. "Single Image Plankton 3D Reconstruction from Extended Depth of Field Shadowgraph". En Pattern Recognition and Information Forensics, 76–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05792-3_8.

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Benammar, Nassima, Haithem Kahil, Anas Titah, Facundo M. Calcagno, Amna Abidi y Mouna Ben Mabrouk. "Improving 3D Plankton Image Classification with C3D2 Architecture and Context Metadata". En 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.

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Mashkov, Oleg, Victoria Kosenko, Nataliia Savina, Yuriy Rozov, Svitlana Radetska y Mariia Voronenko. "Information Technologies for Environmental Monitoring of Plankton Algae Distribution Based on Satellite Image Data". En 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.

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Sieracki, Michael E. y L. Kenneth Webb. "The Application of Image Analysed Fluorescence Microscopy for Characterising Planktonic Bacteria and Protists". En 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.

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Philippe, Grosjean y Denis Kevin. "Supervised Classification of Images, Applied to Plankton Samples Using R and Zooimage". En Data Mining Applications with R, 331–65. Elsevier, 2014. http://dx.doi.org/10.1016/b978-0-12-411511-8.00013-x.

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Actas de conferencias sobre el tema "Plankton images"

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Alfano, Paolo Didier, Marco Rando, Marco Letizia, Francesca Odone, Lorenzo Rosasco y Vito Paolo Pastore. "Efficient Unsupervised Learning for Plankton Images". En 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956360.

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Ma, Wenqi, Tao Chen, Zhengwen Zhang, Zhenyu Yang, Chao Dong, Jianping Qiao y Jianping Li. "Super-resolution for in situ Plankton Images". En 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00411.

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Pu, Yuchun, Zhenghui Feng, Zhonglei Wang, Zhenyu Yang y Jianping Li. "Anomaly Detection for In situ Marine Plankton Images". En 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00409.

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Cheng, Kaichang, Xuemin Cheng y Qun Hao. "A Review of Feature Extraction Technologies for Plankton Images". En the 2018 International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3292425.3293462.

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Goulart, Antonio Jose Homsi, Alexandre Morimitsu, Renan Jacomassi, Nina Hirata y Rubens Lopes. "Deep learning and t-SNE projection for plankton images clusterization". En OCEANS 2021: San Diego – Porto. IEEE, 2021. http://dx.doi.org/10.23919/oceans44145.2021.9706043.

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Sun, Qiantai, Xinwei Wang, Jianan Chen, Liang Sun, Pingshun Lei, Jun He y Yan Zhou. "Automatic region of interest extraction in underwater plankton darkfield images". En Imaging Detection and Target Recognition, editado por Jiangtao Xu y Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3020055.

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Zimmerman, Thomas G., Johnny K. Duong, Ziah Dean, Simone Bianco y Raymond Esquerra. "Evaluating automated reconstruction methods for digital inline holographic images of plankton". En Practical Holography XXXVI: Displays, Materials, and Applications, editado por Hans I. Bjelkhagen y Seung-Hyun Lee. SPIE, 2022. http://dx.doi.org/10.1117/12.2612438.

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Navarro, Gabriel y Javier Ruiz. "Elements of spatial and temporal variability of plankton in the gulf of Cadiz: an analysis based on EOF decomposition of SeaWiFS images". En Remote Sensing, editado por Charles R. Bostater, Jr. y Rosalia Santoleri. SPIE, 2004. http://dx.doi.org/10.1117/12.514031.

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Beemer, Ryan D., Alexandre N. Bandini-Maeder, Jeremy Shaw, Ulysse Lebrec y Mark J. Cassidy. "The Granular Structure of Two Marine Carbonate Sediments". En 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.

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Calcareous sediments are prominent throughout the low-latitudinal offshore environment and have been known to be problematic for offshore foundation systems. These fascinating soils consist largely of the skeletal remains of single-celled marine organisms (plankton and zooplankton) and can be as geologically complex as their onshore siliceous counter parts. To enable an adequate understanding of their characteristics, in particular, their intra-granular micro-structure, it is important that geotechnical engineers do not forget about the multifaceted biological origins of these calcareous sediments and the different geological processes that created them. In this paper, the 3D models of soils grains generated from micro-computed tomography scans, scanning electeron microscope images, and optical microscope images of two calcareous sediments from two different depositional environments are presented and their geotechnical implications discussed. One is a coastal bioclastic sediment from Perth, Western Australia that is geologically similar to carbonate sediments typically used in micro-mechanics and particle crushing studies in the literature. The other is a hemipelagic sediment from a region of the North West Shelf of Australia that has historically been geotechnically problematic for engineers. The results show there is a marked difference between coastal bioclastic and hemipelagic sediments in terms of geological context and the associated particle micro-structures. This brings into question whether a coastal bioclastic calcareous sediment is a good micro-mechanical substitute for a hemipelagic one.
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Marshall, Lauren, Adam Schroeder y Brian Trease. "Comparing Fish-Inspired Ram Filters for Collection of Harmful Algae". En ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88797.

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In this work, several different bioinspired filter geometries are proposed, fabricated, and tested in a flow tank. A novel approach is explored that mimics how filter-feeding fish efficiently remove small food particles from water. These filters generally take the form of a cone with water entering the large end of the cone and exiting through mesh-covered slots in the side of the cone, which emulates the rib structure of these filter-feeding fish. The flow in and around the filters is characterized and their ability to collect algae-scale, neutrally-buoyant particles is evaluated. Filter performance is evaluated by using image processing to count the number of particles collected and studying how the particles are deposited on the filter. Results are presented in the form of particle collection efficiencies, which is a ratio of particles collected to the particles that would nominally enter the filter inlet, and images of the fluorescent particles deposited on the filter at different time intervals. The results show little sensitivity to the filters’ inlet geometries, which was the major difference between filters tested. Comparative results are also presented from a 2D CFD model of the filters generated in COMSOL. The different geometries may differentiate themselves more at larger Reynolds numbers, and it is believed that a fluid exit ratio, or ratio of inlet area to exit area, is the most critical filter parameter. Field testing has demonstrated collection of real algae (i) with this bioinspired filter, and (ii) from a robot platform, but using a more conventional plankton net. The larger vision is to develop these filters and mount them on a swarm of autonomous surface vehicles, i.e. a robot boat swarm, which is being developed in parallel.
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Informes sobre el tema "Plankton images"

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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, julio de 2021. http://dx.doi.org/10.1575/1912/27377.

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This technical manual guides the user through the process of creating a data table for the submission of taxonomic and morphological information for plankton and other particles from images to a repository. Guidance is provided to produce documentation that should accompany the submission of plankton and other particle data to a repository, describes data collection and processing techniques, and outlines the creation of a data file. Field names include scientificName that represents the lowest level taxonomic classification (e.g., genus if not certain of species, family if not certain of genus) and scientificNameID, the unique identifier from a reference database such as the World Register of Marine Species or AlgaeBase. The data table described here includes the field names associatedMedia, scientificName/ scientificNameID for both automated and manual identification, biovolume, area_cross_section, length_representation and width_representation. Additional steps that instruct the user on how to format their data for a submission to the Ocean Biodiversity Information System (OBIS) are also included. Examples of documentation and data files are provided for the user to follow. The documentation requirements and data table format are approved by both NASA’s SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and the National Science Foundation’s Biological and Chemical Oceanography Data Management Office (BCO-DMO).
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