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Pattern Recognition Applications and Methods : 9th International Conference, ICPRAM 2020, Valletta, Malta, February 22-24, 2020, Revised Selected Papers / edited by Maria De Marsico, Gabriella Sanniti di Baja, Ana Fred.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12594
- Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12594
- Language:
- English
- Subjects (All):
- Pattern recognition systems.
- Artificial intelligence.
- Computer vision.
- Computer engineering.
- Computer networks.
- Automated Pattern Recognition.
- Artificial Intelligence.
- Computer Vision.
- Computer Engineering and Networks.
- Local Subjects:
- Automated Pattern Recognition.
- Artificial Intelligence.
- Computer Vision.
- Computer Engineering and Networks.
- Physical Description:
- 1 online resource (XI, 139 pages) : 46 illustrations, 41 illustrations in color.
- Edition:
- 1st ed. 2020.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2020.
- System Details:
- text file PDF
- Summary:
- This book contains revised and extended versions of selected papers from the 9th International Conference on Pattern Recognition, ICPRAM 2020, held in Valletta, Malta, in February 2020. The 7 full papers presented were carefully reviewed and selected from 102 initial submissions. The papers describe applications of pattern recognition techniques to real-world problems, interdisciplinary research, experimental and theoretical studies yielding new insights that advance pattern recognition methods are especially encouraged.
- Contents:
- End to End Deep Neural Network Classifier Design for Universal Sign Recognition
- MaskADNet: MOTS based on ADNet
- Dimensionality Reduction and Attention Mechanisms for Extracting
- Efficient Radial Distortion Correction for Planar Motion
- Comparison of algorithms for Tree-top detection in Drone image mosaics of Japanese Mixed Forests
- Investigating Similarity Metrics for Convolutional Neural Networks in the Case of Unstructured Pruning
- Encoding of Indefinite Proximity Data: A Structure Preserving Perspective.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-66125-0
- 9783030661250
- Access Restriction:
- Restricted for use by site license.
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