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Titlebook: Image and Signal Processing; 9th International Co Abderrahim El Moataz,Driss Mammass,Fathallah Noubo Conference proceedings 2020 Springer N

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发表于 2025-3-21 17:12:35 | 显示全部楼层 |阅读模式
书目名称Image and Signal Processing
副标题9th International Co
编辑Abderrahim El Moataz,Driss Mammass,Fathallah Noubo
视频videohttp://file.papertrans.cn/462/461504/461504.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Image and Signal Processing; 9th International Co Abderrahim El Moataz,Driss Mammass,Fathallah Noubo Conference proceedings 2020 Springer N
描述This volume constitutes the refereed proceedings of the 9th International Conference on Image and Signal Processing, ICISP 2020, which was due to be held in Marrakesh, Morocco, in June 2020. The conference was cancelled due to the COVID-19 pandemic..The 40 revised full papers were carefully reviewed and selected from 84 submissions. The contributions presented in this volume were organized in the following  topical sections: digital cultural heritage & color and spectral imaging; data and image processing for precision agriculture; machine learning application and innovation; biomedical imaging; deep learning and applications; pattern recognition; segmentation and retrieval; mathematical imaging & signal processing..
出版日期Conference proceedings 2020
关键词artificial intelligence; classification; computer hardware; computer networks; computer systems; computer
版次1
doihttps://doi.org/10.1007/978-3-030-51935-3
isbn_softcover978-3-030-51934-6
isbn_ebook978-3-030-51935-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Vine Disease Detection by Deep Learning Method Combined with 3D Depth Informationrk SegNet and a depth map (DM), to delineate the asymptomatic regions in the vine canopy. The results obtained showed that SegNet combined with the depth information give better accuracy than a SegNet segmentation alone.
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Incep-EEGNet: A ConvNet for Motor Imagery Decodinghat decodes the raw signal to achieve state-of-the-art performances using an architecture based on Inception. The obtained results show that our method outperforms state-of-the-art filter bank common spatial patterns (FBCSP) and ShallowConvNet on based on the dataset IIa of the BCI Competition IV.
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0302-9743 cal imaging; deep learning and applications; pattern recognition; segmentation and retrieval; mathematical imaging & signal processing..978-3-030-51934-6978-3-030-51935-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Approach to Analysis the Surface Geometry Change in Cultural Heritage Objectsmical cleaning for a chosen object. The new local neighborhood distance histogram has been proposed as a local measure of surface change based on optimized k-neighborhood search algorithm to assess the local geometry change of a focus point.
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A Spectral Hazy Image Databasees the design of spectral-based image dehazing able to handle simultaneously the accurate estimation of the parameters of the visibility degradation model and the limitation of artifacts and post-dehazing noise. The database can be downloaded freely at ..
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Deep Transfer Learning Models for Tomato Disease Detectionmages of infected plant leaves divided into 6 types of infections pest attacks and plant diseases. The results were promising with an accuracy up to 95.65% for DensNet161, 94.93% for DensNet121 and 90.58% for VGG16.
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Big Data and Reality Mining in Healthcare: Promise and Potentialtterns and treatment monitoring. We illustrate the benefits of reality mining analytics that lead to promote patients’ health, enhance medicine, reduce cost and improve healthcare value and quality. Furthermore, we highlight some challenges that big data analytics faces in healthcare.
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A Bottom-Up Approach for Pig Skeleton Extraction Using RGB Dataetwork, keyframes are selected from the test data using K-mean sampler. In total, 9 Keypoints are annotated that gives a brief detailed behavior analysis in the farm setting. Extensive experiments are conducted and the quantitative results show that the network has the potential of increasing the tracking performance by a substantial margin.
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