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Titlebook: Computer Analysis of Images and Patterns; 17th International C Michael Felsberg,Anders Heyden,Norbert Krüger Conference proceedings 2017 Sp

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書目名稱Computer Analysis of Images and Patterns
副標題17th International C
編輯Michael Felsberg,Anders Heyden,Norbert Krüger
視頻videohttp://file.papertrans.cn/234/233445/233445.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Analysis of Images and Patterns; 17th International C Michael Felsberg,Anders Heyden,Norbert Krüger Conference proceedings 2017 Sp
描述The two volume set LNCS 10424 and 10425 constitutes the refereed proceedings of the 17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017, held in Ystad, Sweden, in August 2017. .The 72 papers presented were carefully reviewed and selected from 144 submissions The papers are organized in the following topical sections: Vision for Robotics; Motion and Tracking; Segmentation; Image/Video Indexing and Retrieval; Shape Representation and Analysis; Biomedical Image Analysis; Biometrics; Machine Learning; Image Restoration; and Poster Sessions..
出版日期Conference proceedings 2017
關鍵詞Computer vision; Image segmentation; Object recognition; Image reconstruction; Biomedical image and patt
版次1
doihttps://doi.org/10.1007/978-3-319-64689-3
isbn_softcover978-3-319-64688-6
isbn_ebook978-3-319-64689-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

書目名稱Computer Analysis of Images and Patterns影響因子(影響力)




書目名稱Computer Analysis of Images and Patterns影響因子(影響力)學科排名




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書目名稱Computer Analysis of Images and Patterns網絡公開度學科排名




書目名稱Computer Analysis of Images and Patterns被引頻次




書目名稱Computer Analysis of Images and Patterns被引頻次學科排名




書目名稱Computer Analysis of Images and Patterns年度引用




書目名稱Computer Analysis of Images and Patterns年度引用學科排名




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書目名稱Computer Analysis of Images and Patterns讀者反饋學科排名




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Deep Projective 3D Semantic Segmentationion scores are re-projected to the point cloud to obtain the segmentation results. We further investigate the impact of multiple modalities, such as color, depth and surface normals, in a multi-stream network architecture. Experiments are performed on the recent Semantic3D dataset. Our approach sets
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Robust Long-Term Aerial Video Mosaicking by Weighted Feature-Based Global Motion Estimationrespondences of highest possible quality. Thirdly, we propose a temporally variable tracking distance approach to remove outliers located at slowly moving objects..As a result we improve the . accuracy by 10% for synthetic data and highly reduce the structural dissimilarity (.) caused by stitching e
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Prolongements des mesures aletoires, Robust Features (SURF) were also used. According to results, SCM was the fastest feature extractor with 0.117?s and accuracy of 100% in navigation test, showing the relevance of our approach in the mobile robot localization.
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https://doi.org/10.1007/BFb0059328erspectral data in terms of illumination, which yields vast improvements in classification accuracy, preventing most errors caused by shading and other influences. Furthermore we utilize Gabor texture features which add spatial information to the feature space without increasing the data dimensional
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Conference proceedings 2017atterns, CAIP 2017, held in Ystad, Sweden, in August 2017. .The 72 papers presented were carefully reviewed and selected from 144 submissions The papers are organized in the following topical sections: Vision for Robotics; Motion and Tracking; Segmentation; Image/Video Indexing and Retrieval; Shape
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Un dimanche de juin avec Jacquese inferred jointly in our formulation. Experiments are performed on three challenging tracking benchmarks: OTB-2015, TempleColor and VOT2016. Our approach improves the baseline method, leading to performance comparable to state-of-the-art.
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