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Erschienen in:
Metallographic Analysis of the Percentage of Carbon in the Test Tube Based on Artificial Vision Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

34. Deep Learning for Robot Vision

verfasst von : Mamilla Keerthikeshwar, S. Anto

Erschienen in: Intelligent Manufacturing and Energy Sustainability

Verlag: Springer Singapore

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Abstract

Deep learning comes under a class of machine learning where we use it for extremely high-level output, like recognition of images, etc. It has been used in pattern recognition over a vast area such as handmade crafts to extract the data from learning procedures. At present, it has gained a great significance in robot vision. In this paper, we show how neural networks play a vital role in robot vision. Image segmentation, which is the initial step, is used to preprocess the images and videos. The multilayered artificial neural networks have a lot more applications. It can be applied in drug detection, military bases, and many more. The main objective of this paper is to review how deep learning algorithms and deep nets can be used in various areas of robot vision. There are some predefined deep learning algorithms that are available in the market, which are used here to perform this comparative study. These will help us to have a clear insight while building vision systems using deep learning.

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Vorheriges Kapitel Medicinal Leaves Recognition Using Contour-Based Segmentation
Nächstes Kapitel Deep Learning Approach for Prediction of Handwritten Telugu Vowels
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Metadaten
Titel
Deep Learning for Robot Vision
verfasst von
Mamilla Keerthikeshwar
S. Anto
Copyright-Jahr
2021
Verlag
Springer Singapore
Buch
Intelligent Manufacturing and Energy Sustainability

Print ISBN: 978-981-334-442-6

Electronic ISBN: 978-981-334-443-3

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-981-33-4443-3_34

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