Literatura científica selecionada sobre o tema "Invariance des colorations"
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Artigos de revistas sobre o assunto "Invariance des colorations"
Gayathri, V., Eric Clapten, S. Mahalakshmi e S. Rajes Kannan. "Color and Texture Feature Based Scene Classification". Journal of Computational and Theoretical Nanoscience 17, n.º 11 (1 de novembro de 2020): 4897–901. http://dx.doi.org/10.1166/jctn.2020.9254.
Texto completo da fonteDe Bortoli, Gian Marco, Karolina Prawda e Sebastian J. Schlecht. "Active Acoustics with a Phase Cancelling Modal Reverberator". Journal of the Audio Engineering Society 72, n.º 10 (16 de outubro de 2024): 705–15. http://dx.doi.org/10.17743/jaes.2022.0171.
Texto completo da fonteImperiali, 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.
Texto completo da fonteGURSKAYA, Nadya G., Arkady F. FRADKOV, Natalia I. POUNKOVA, Dmitry B. STAROVEROV, Maria E. BULINA, Yurii G. YANUSHEVICH, Yulii A. LABAS, Sergey LUKYANOV e Konstantin A. LUKYANOV. "A colourless green fluorescent protein homologue from the non-fluorescent hydromedusa Aequorea coerulescens and its fluorescent mutants". Biochemical Journal 373, n.º 2 (15 de julho de 2003): 403–8. http://dx.doi.org/10.1042/bj20021966.
Texto completo da fonteMøller, Anders Pape, Wei Liang e Diogo S. M. Samia. "Flight initiation distance, color and camouflage". Current Zoology 65, n.º 5 (22 de fevereiro de 2019): 535–40. http://dx.doi.org/10.1093/cz/zoz005.
Texto completo da fonteHämäläinen, Liisa, Georgina E. Binns, Nathan S. Hart, Johanna Mappes, Paul G. McDonald, Louis G. O’Neill, Hannah M. Rowland, Kate D. L. Umbers e Marie E. Herberstein. "Predator selection on multicomponent warning signals in an aposematic moth". Behavioral Ecology, 16 de novembro de 2023. http://dx.doi.org/10.1093/beheco/arad097.
Texto completo da fonteHuang, 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, 5 de junho de 2023. http://dx.doi.org/10.1088/2058-8585/acdb84.
Texto completo da fonteLin, Jhan-Wei, Ying-Rong Chen, Tsui-Wen Li, Pei-Jen L. Shaner e Si-Min Lin. "Long-term monitoring reveals invariant clutch size and unequal reproductive costs between sexes in a subtropical lacertid lizard". Zoological Letters 6, n.º 1 (6 de janeiro de 2020). http://dx.doi.org/10.1186/s40851-019-0152-0.
Texto completo da fonteShang, Xiao, Guicang He, Longjie Li, Chong Wang, Cheng Lu, Peiwen Zhang, Jiebin Niu e Lina Shi. "Controlling brightness in full color nanoprinting by all-dielectric metasurfaces". Applied Physics Letters 122, n.º 18 (1 de maio de 2023). http://dx.doi.org/10.1063/5.0143215.
Texto completo da fonteHayakawa, Hisashi, Yusuke Ebihara e Alexei A. Pevtsov. "Analyses of Equatorward Auroral Extensions during the Extreme Geomagnetic Storm on 15 July 1959". Monthly Notices of the Royal Astronomical Society, 17 de novembro de 2023. http://dx.doi.org/10.1093/mnras/stad3556.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteA 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