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Titlebook: Structural, Syntactic, and Statistical Pattern Recognition; Joint IAPR Internati Adam Krzyzak,Ching Y. Suen,Nicola Nobile Conference procee

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發(fā)表于 2025-3-21 20:05:46 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Structural, Syntactic, and Statistical Pattern Recognition
副標(biāo)題Joint IAPR Internati
編輯Adam Krzyzak,Ching Y. Suen,Nicola Nobile
視頻videohttp://file.papertrans.cn/881/880083/880083.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Structural, Syntactic, and Statistical Pattern Recognition; Joint IAPR Internati Adam Krzyzak,Ching Y. Suen,Nicola Nobile Conference procee
描述This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2022, held in Montreal, QC, Canada, in August 2022..The 30 papers together with 2 invited talks presented in this volume were carefully reviewed and selected from 50 submissions. The workshops presents papers on topics such as deep learning, processing, computer vision, machine learning and pattern recognition and much more. .
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; computer networks; computer science; computer systems; computer vision; database
版次1
doihttps://doi.org/10.1007/978-3-031-23028-8
isbn_softcover978-3-031-23027-1
isbn_ebook978-3-031-23028-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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,Annotation-Free Keyword Spotting in?Historical Vietnamese Manuscripts Using Graph Matching,orical Vietnamese manuscripts containing 719 scanned pages of the famous Tale of Kieu. Our results show that search terms can be found with promising precision both when providing handwritten samples (query by example) as well as printed characters (query by string).
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Spatio-Temporal United Memory for Video Anomaly Detection,e AUC 96.92%, 87.43%, and 75.42% on UCSD Ped2, Avenue, and ShanghaiTech, respectively. Extensive experiments on three publicly available datasets demonstrate the excellent generalization and high effectiveness of the proposed method.
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Self-supervised Out-of-Distribution Detection with Dynamic Latent Scale GAN,ual information of in-distribution data, and additional hyperparameters for prediction. The proposed method showed better out-of-distribution detection performance than the previous state-of-art method.
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,Monte Carlo Dropout for?Uncertainty Analysis and?ECG Trace Image Classification,rld scenarios. We use ECG images dataset of cardiac and covid-19 patients containing five categories of data, which includes COVID-19 ECG records as well as data from other cardiovascular disorders. Our proposed model achieves 93.90% accuracy using this dataset.
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