Benchmarking different deep regression models for predicting image rotation angle and robot’s end effector’s position

Deep visual regression models have an important role to find how much the learning model fits the relationship between the visual data (images) and the predicted continuous output. Recently, deep visual regression has been utilized in different applications such as age prediction, digital holography...

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Main Authors:	AlDahoul, Nouar, Mohd Suhaimi, Nur Farahana
Format:	Conference or Workshop Item
Language:	English English
Published:	Institute of Electrical and Electronics Engineers Inc. 2019
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/78088/ http://irep.iium.edu.my/78088/ http://irep.iium.edu.my/78088/ http://irep.iium.edu.my/78088/1/78088_Benchmarking%20different%20deep%20regression%20models_complete_new.pdf http://irep.iium.edu.my/78088/2/78088_Benchmarking%20different%20deep%20regression%20models_scopus.pdf

id	iium-78088
recordtype	eprints
spelling	iium-780882020-02-15T11:24:19Z http://irep.iium.edu.my/78088/ Benchmarking different deep regression models for predicting image rotation angle and robot’s end effector’s position AlDahoul, Nouar Mohd Suhaimi, Nur Farahana T Technology (General) Deep visual regression models have an important role to find how much the learning model fits the relationship between the visual data (images) and the predicted continuous output. Recently, deep visual regression has been utilized in different applications such as age prediction, digital holography, and head-pose estimation. Deep learning has recently been cutting-edge research. Most of the research papers have focused on utilizing deep learning in classification tasks. There is still a lack of research that use deep learning for regression. This paper utilizes different deep learning models for two regression tasks. The first one is the prediction of the image rotation angle. The second task is to predict the position of the robot’s end-effector in 2D space. Efficient features were learned or extracted in order to perform good regression. The paper demonstrates and compares various models such as a local Receptive Field-Extreme Learning Machine (LRF-ELM), Hierarchical ELM, Supervised Convolutional Neural Network (CNN), and pre-trained CNN such as AlexNet. Each model was trained to learn or extract features and map them to specific continuous output. The results show that all models gave good performance in terms of RMSE and accuracy. H-ELM was found to outperform other models in term of training speed. Institute of Electrical and Electronics Engineers Inc. 2019-10 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/78088/1/78088_Benchmarking%20different%20deep%20regression%20models_complete_new.pdf application/pdf en http://irep.iium.edu.my/78088/2/78088_Benchmarking%20different%20deep%20regression%20models_scopus.pdf AlDahoul, Nouar and Mohd Suhaimi, Nur Farahana (2019) Benchmarking different deep regression models for predicting image rotation angle and robot’s end effector’s position. In: 7th International Conference on Mechatronics Engineering, ICOM 2019, Putrajaya. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8952047 10.1109/ICOM47790.2019.8952047
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General)
spellingShingle	T Technology (General) AlDahoul, Nouar Mohd Suhaimi, Nur Farahana Benchmarking different deep regression models for predicting image rotation angle and robot’s end effector’s position
description	Deep visual regression models have an important role to find how much the learning model fits the relationship between the visual data (images) and the predicted continuous output. Recently, deep visual regression has been utilized in different applications such as age prediction, digital holography, and head-pose estimation. Deep learning has recently been cutting-edge research. Most of the research papers have focused on utilizing deep learning in classification tasks. There is still a lack of research that use deep learning for regression. This paper utilizes different deep learning models for two regression tasks. The first one is the prediction of the image rotation angle. The second task is to predict the position of the robot’s end-effector in 2D space. Efficient features were learned or extracted in order to perform good regression. The paper demonstrates and compares various models such as a local Receptive Field-Extreme Learning Machine (LRF-ELM), Hierarchical ELM, Supervised Convolutional Neural Network (CNN), and pre-trained CNN such as AlexNet. Each model was trained to learn or extract features and map them to specific continuous output. The results show that all models gave good performance in terms of RMSE and accuracy. H-ELM was found to outperform other models in term of training speed.
format	Conference or Workshop Item
author	AlDahoul, Nouar Mohd Suhaimi, Nur Farahana
author_facet	AlDahoul, Nouar Mohd Suhaimi, Nur Farahana
author_sort	AlDahoul, Nouar
title	Benchmarking different deep regression models for predicting image rotation angle and robot’s end effector’s position
title_short	Benchmarking different deep regression models for predicting image rotation angle and robot’s end effector’s position
title_full	Benchmarking different deep regression models for predicting image rotation angle and robot’s end effector’s position
title_fullStr	Benchmarking different deep regression models for predicting image rotation angle and robot’s end effector’s position
title_full_unstemmed	Benchmarking different deep regression models for predicting image rotation angle and robot’s end effector’s position
title_sort	benchmarking different deep regression models for predicting image rotation angle and robot’s end effector’s position
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2019
url	http://irep.iium.edu.my/78088/ http://irep.iium.edu.my/78088/ http://irep.iium.edu.my/78088/ http://irep.iium.edu.my/78088/1/78088_Benchmarking%20different%20deep%20regression%20models_complete_new.pdf http://irep.iium.edu.my/78088/2/78088_Benchmarking%20different%20deep%20regression%20models_scopus.pdf
first_indexed	2023-09-18T21:50:03Z
last_indexed	2023-09-18T21:50:03Z
_version_	1777413708889194496

Benchmarking different deep regression models for predicting image rotation angle and robot’s end effector’s position

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