Artificial Neural Networks in Pattern Recognition : 9th IAPR TC3 Workshop, ANNPR 2020, Winterthur, Switzerland, September 2-4, 2020, Proceedings / edited by Frank-Peter Schilling, Thilo Stadelmann.

Format:

Book

Contributor:

Schilling, F.-P. (Frank-Peter), Editor.

Stadelmann, Thilo, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 12294

Lecture Notes in Artificial Intelligence, 2945-9141 ; 12294

Language:

English

Subjects (All):

Artificial intelligence.

Computer vision.

Data mining.

Pattern recognition systems.

Image processing-Digital techniques.

Artificial Intelligence.

Computer Vision.

Data Mining and Knowledge Discovery.

Automated Pattern Recognition.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Local Subjects:

Artificial Intelligence.

Computer Vision.

Data Mining and Knowledge Discovery.

Automated Pattern Recognition.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Physical Description:

1 online resource (XI, 306 pages) : 205 illustrations, 114 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 constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications.

Contents:

Deep Learning Methods for Image Guidance in Radiation Therapy Intentional Image Similarity Search

Sttructured (De)composable Representations Trained with Neural Networks

Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling

Improving Accuracy and Efficiency of Object Detection Algorithms using Multiscale Feature Aggregation Plugins

Abstract Echo State Networks

Minimal Complexity Support Vector Machines

Named Entity Disambiguation at Scale

Geometric Attention for Prediction of Differential Properties in 3D Point Clouds

How (Not) to Measure Bias in Face Recognition Networks.-Feature Extraction: A Time Window Analysis based on the X-ITE Pain Database

Pain Intensity Recognition - An Analysis of Short-Time Sequences in a Real-World Scenario

A deep learning approach for efficient registration of dual view mammography

Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology

Applications of Generative Adversarial Networks to Dermatologic Imaging

Typing Plasmids with Distributed Sequence Representation

KP-YOLO: a modification of YOLO algorithm for the keypoint-based detection of QR Codes

Using Mask R-CNN for Image-Based Wear Classification of Solid Carbide Milling and Drilling Tools

A Hybrid Deep Learning Approach For Forecasting Air Temperature

Using CNNs to optimize numerical simulations in geotechnical engineering

Going for 2D or 3D? Investigating various Machine Learning Approaches for Peach Variety Identification

A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture

ML-Based Trading Models: An investigation during COVID-19 pandemic crisis

iNNvestigate-GUI - Explaining Neural Networks Through an Interactive Visualization Tool.

Other Format:

Printed edition:

ISBN:

978-3-030-58309-5

9783030583095

Access Restriction:

Restricted for use by site license.