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Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Comparative Research on SOM with Torus and Sphere Topologies for Peculiarity Classification of Flat Finishing Skill Training

Authors : Masaru Teranishi, Shimpei Matsumoto, Hidetoshi Takeno

Published in: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing

Publisher: Springer International Publishing

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Abstract

The paper compares classification performances on Self-Organizing Maps (SOMs) by torus and spherical topologies in the case of peculiarities classification of flat finishing motion with an iron file measured by a 3D stylus. In case of manufacturing skill training, peculiarities of tool motion are useful information for learners. Classified peculiarities are also useful especially for trainers to grasp effectively the tendency of the learners’ peculiarities in their class. In the authors’ former studies, a torus SOM are considered to be powerful tools to classify and visualize peculiarities with its borderless topological feature map structure. In this paper, the authors compare the classification performance of two kind of borderless topological SOMs: torus SOM and spherical SOM by quality measurements.

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previous chapter LSTM with Uniqueness Attention for Human Activity Recognition
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Metadata
Title
Comparative Research on SOM with Torus and Sphere Topologies for Peculiarity Classification of Flat Finishing Skill Training
Authors
Masaru Teranishi
Shimpei Matsumoto
Hidetoshi Takeno
Copyright Year
2019
Book
Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing

Print ISBN: 978-3-030-30507-9

Electronic ISBN: 978-3-030-30508-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-30508-6_41

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