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Academic literature on the topic 'Invariance des colorations'
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Journal articles on the topic "Invariance des colorations"
Gayathri, V., Eric Clapten, S. Mahalakshmi, and S. Rajes Kannan. "Color and Texture Feature Based Scene Classification." Journal of Computational and Theoretical Nanoscience 17, no. 11 (November 1, 2020): 4897–901. http://dx.doi.org/10.1166/jctn.2020.9254.
Full textDe Bortoli, Gian Marco, Karolina Prawda, and Sebastian J. Schlecht. "Active Acoustics with a Phase Cancelling Modal Reverberator." Journal of the Audio Engineering Society 72, no. 10 (October 16, 2024): 705–15. http://dx.doi.org/10.17743/jaes.2022.0171.
Full textImperiali, Christophe. "« La plus admirable personnification du génie germanique » : Faust comme stéréotype de la germanité." Revue germanique internationale 39 (2024): 45–68. http://dx.doi.org/10.4000/123f2.
Full textGURSKAYA, Nadya G., Arkady F. FRADKOV, Natalia I. POUNKOVA, Dmitry B. STAROVEROV, Maria E. BULINA, Yurii G. YANUSHEVICH, Yulii A. LABAS, Sergey LUKYANOV, and Konstantin A. LUKYANOV. "A colourless green fluorescent protein homologue from the non-fluorescent hydromedusa Aequorea coerulescens and its fluorescent mutants." Biochemical Journal 373, no. 2 (July 15, 2003): 403–8. http://dx.doi.org/10.1042/bj20021966.
Full textMøller, Anders Pape, Wei Liang, and Diogo S. M. Samia. "Flight initiation distance, color and camouflage." Current Zoology 65, no. 5 (February 22, 2019): 535–40. http://dx.doi.org/10.1093/cz/zoz005.
Full textHämäläinen, Liisa, Georgina E. Binns, Nathan S. Hart, Johanna Mappes, Paul G. McDonald, Louis G. O’Neill, Hannah M. Rowland, Kate D. L. Umbers, and Marie E. Herberstein. "Predator selection on multicomponent warning signals in an aposematic moth." Behavioral Ecology, November 16, 2023. http://dx.doi.org/10.1093/beheco/arad097.
Full textHuang, Lipeng, Xiaolian Chen, Xinzhou Wu, Zizhou Hu, Shuhong Nie, Chenchao Huang, Shuo Zhang, et al. "Hybrid Ag/Ni Mesh/PH 1000 Transparent Electrodes for High Performance Flexible Electrochromic Devices with Exceptional Stability." Flexible and Printed Electronics, June 5, 2023. http://dx.doi.org/10.1088/2058-8585/acdb84.
Full textLin, Jhan-Wei, Ying-Rong Chen, Tsui-Wen Li, Pei-Jen L. Shaner, and Si-Min Lin. "Long-term monitoring reveals invariant clutch size and unequal reproductive costs between sexes in a subtropical lacertid lizard." Zoological Letters 6, no. 1 (January 6, 2020). http://dx.doi.org/10.1186/s40851-019-0152-0.
Full textShang, Xiao, Guicang He, Longjie Li, Chong Wang, Cheng Lu, Peiwen Zhang, Jiebin Niu, and Lina Shi. "Controlling brightness in full color nanoprinting by all-dielectric metasurfaces." Applied Physics Letters 122, no. 18 (May 1, 2023). http://dx.doi.org/10.1063/5.0143215.
Full textHayakawa, Hisashi, Yusuke Ebihara, and Alexei A. Pevtsov. "Analyses of Equatorward Auroral Extensions during the Extreme Geomagnetic Storm on 15 July 1959." Monthly Notices of the Royal Astronomical Society, November 17, 2023. http://dx.doi.org/10.1093/mnras/stad3556.
Full textDissertations / Theses on the topic "Invariance des colorations"
Nisar, Zeeshan. "Self-supervised learning in the presence of limited labelled data for digital histopathology." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD016.
Full textA key challenge in applying deep learning to histopathology is the variation in stainings, both inter and intra-stain. Deep learning models trained on one stain (or domain) often fail on others, even for the same task (e.g., kidney glomeruli segmentation). Labelling each stain is expensive and time-consuming, prompting researchers to explore domain adaptation based stain-transfer methods. These aim to perform multi-stain segmentation using labels from only one stain but are limited by the introduction of domain shift, reducing performance. Detecting and quantifying this domain shift is important. This thesis focuses on unsupervised methods to develop a metric for detecting domain shift and proposes a novel stain-transfer approach to minimise it. While multi-stain algorithms reduce the need for labels in target stains, they may struggle with tissue types lacking source-stain labels. To address this, the thesis focuses to improve multi-stain segmentation with less reliance on labelled data using self-supervision. While this thesis focused on kidney glomeruli segmentation, the proposed methods are designed to be applicable to other histopathology tasks and domains, including medical imaging and computer vision