An implementation of an affine BRISK for mobile heterogeneous parallel processors

Chulhee Lee; Chae Eun Rhee; Hyuk Jae Lee

doi:10.1109/ICCE.2016.7430680

An implementation of an affine BRISK for mobile heterogeneous parallel processors

Chulhee Lee, Chae Eun Rhee, Hyuk Jae Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

As the computing power of a mobile processor increases, a number of computer vision applications are widely used by a mobile device. Feature extraction is one of the crucial tasks in a computer vision area and it is used for object recognition, panorama image generation and so on. The affine SIFT (ASIFT) feature is robust to scale, rotation and viewpoint changes but its high computational complexity makes it challenging for ASIFT to be used in applications by a mobile device. To reduce the complexity of ASIFT while maintaining affine invariance, this paper proposes an affine BRISK which replaces SIFT by BRISK of which complexity is much less than SIFT. By fully exploiting the parallelism of affine transformation for viewpoint invariance, this paper achieves the speed up by parallel execution of feature extraction and affine transformation running on the mobile CPU and GPU, respectively. The proposed affine BRISK feature is robust to a viewpoint change with a comparable accuracy. The processing time is 290.64ms on a Galaxy LTE-A mobile.

Original language	English
Title of host publication	2016 IEEE International Conference on Consumer Electronics, ICCE 2016
Editors	Francisco J. Bellido, Daniel Diaz-Sanchez, Nicholas C. H. Vun, Carsten Dolar, Wing-Kuen Ling
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	435-436
Number of pages	2
ISBN (Electronic)	9781467383646
DOIs	https://doi.org/10.1109/ICCE.2016.7430680
Publication status	Published - 2016 Mar 10
Event	IEEE International Conference on Consumer Electronics, ICCE 2016 - Las Vegas, United States Duration: 2016 Jan 7 → 2016 Jan 11

Publication series

Name	2016 IEEE International Conference on Consumer Electronics, ICCE 2016

Other

Other	IEEE International Conference on Consumer Electronics, ICCE 2016
Country	United States
City	Las Vegas
Period	16/1/7 → 16/1/11

Bibliographical note

Funding Information:
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2015-H8501-15-1005) supervised by the IITP(Institute for Information and communications Technology Promotion) and by the Industrial Strategic Technology Development Program (10041664, The Development of Fusion Processor based on Multi-Shader GPU) funded by the Ministry Of Trade, Industry and Energy (MOTIE, Korea)

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Electrical and Electronic Engineering

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10.1109/ICCE.2016.7430680

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Lee, C., Rhee, C. E., & Lee, H. J. (2016). An implementation of an affine BRISK for mobile heterogeneous parallel processors. In F. J. Bellido, D. Diaz-Sanchez, N. C. H. Vun, C. Dolar, & W-K. Ling (Eds.), 2016 IEEE International Conference on Consumer Electronics, ICCE 2016 (pp. 435-436). [7430680] (2016 IEEE International Conference on Consumer Electronics, ICCE 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE.2016.7430680

Lee, Chulhee ; Rhee, Chae Eun ; Lee, Hyuk Jae. / An implementation of an affine BRISK for mobile heterogeneous parallel processors. 2016 IEEE International Conference on Consumer Electronics, ICCE 2016. editor / Francisco J. Bellido ; Daniel Diaz-Sanchez ; Nicholas C. H. Vun ; Carsten Dolar ; Wing-Kuen Ling. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 435-436 (2016 IEEE International Conference on Consumer Electronics, ICCE 2016).

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abstract = "As the computing power of a mobile processor increases, a number of computer vision applications are widely used by a mobile device. Feature extraction is one of the crucial tasks in a computer vision area and it is used for object recognition, panorama image generation and so on. The affine SIFT (ASIFT) feature is robust to scale, rotation and viewpoint changes but its high computational complexity makes it challenging for ASIFT to be used in applications by a mobile device. To reduce the complexity of ASIFT while maintaining affine invariance, this paper proposes an affine BRISK which replaces SIFT by BRISK of which complexity is much less than SIFT. By fully exploiting the parallelism of affine transformation for viewpoint invariance, this paper achieves the speed up by parallel execution of feature extraction and affine transformation running on the mobile CPU and GPU, respectively. The proposed affine BRISK feature is robust to a viewpoint change with a comparable accuracy. The processing time is 290.64ms on a Galaxy LTE-A mobile.",

author = "Chulhee Lee and Rhee, {Chae Eun} and Lee, {Hyuk Jae}",

note = "Funding Information: This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2015-H8501-15-1005) supervised by the IITP(Institute for Information and communications Technology Promotion) and by the Industrial Strategic Technology Development Program (10041664, The Development of Fusion Processor based on Multi-Shader GPU) funded by the Ministry Of Trade, Industry and Energy (MOTIE, Korea) Publisher Copyright: {\textcopyright} 2016 IEEE.; IEEE International Conference on Consumer Electronics, ICCE 2016 ; Conference date: 07-01-2016 Through 11-01-2016",

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Lee, C, Rhee, CE & Lee, HJ 2016, An implementation of an affine BRISK for mobile heterogeneous parallel processors. in FJ Bellido, D Diaz-Sanchez, NCH Vun, C Dolar & W-K Ling (eds), 2016 IEEE International Conference on Consumer Electronics, ICCE 2016., 7430680, 2016 IEEE International Conference on Consumer Electronics, ICCE 2016, Institute of Electrical and Electronics Engineers Inc., pp. 435-436, IEEE International Conference on Consumer Electronics, ICCE 2016, Las Vegas, United States, 16/1/7. https://doi.org/10.1109/ICCE.2016.7430680

An implementation of an affine BRISK for mobile heterogeneous parallel processors. / Lee, Chulhee; Rhee, Chae Eun; Lee, Hyuk Jae.

2016 IEEE International Conference on Consumer Electronics, ICCE 2016. ed. / Francisco J. Bellido; Daniel Diaz-Sanchez; Nicholas C. H. Vun; Carsten Dolar; Wing-Kuen Ling. Institute of Electrical and Electronics Engineers Inc., 2016. p. 435-436 7430680 (2016 IEEE International Conference on Consumer Electronics, ICCE 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Rhee, Chae Eun

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N1 - Funding Information: This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2015-H8501-15-1005) supervised by the IITP(Institute for Information and communications Technology Promotion) and by the Industrial Strategic Technology Development Program (10041664, The Development of Fusion Processor based on Multi-Shader GPU) funded by the Ministry Of Trade, Industry and Energy (MOTIE, Korea) Publisher Copyright: © 2016 IEEE.

PY - 2016/3/10

Y1 - 2016/3/10

N2 - As the computing power of a mobile processor increases, a number of computer vision applications are widely used by a mobile device. Feature extraction is one of the crucial tasks in a computer vision area and it is used for object recognition, panorama image generation and so on. The affine SIFT (ASIFT) feature is robust to scale, rotation and viewpoint changes but its high computational complexity makes it challenging for ASIFT to be used in applications by a mobile device. To reduce the complexity of ASIFT while maintaining affine invariance, this paper proposes an affine BRISK which replaces SIFT by BRISK of which complexity is much less than SIFT. By fully exploiting the parallelism of affine transformation for viewpoint invariance, this paper achieves the speed up by parallel execution of feature extraction and affine transformation running on the mobile CPU and GPU, respectively. The proposed affine BRISK feature is robust to a viewpoint change with a comparable accuracy. The processing time is 290.64ms on a Galaxy LTE-A mobile.

AB - As the computing power of a mobile processor increases, a number of computer vision applications are widely used by a mobile device. Feature extraction is one of the crucial tasks in a computer vision area and it is used for object recognition, panorama image generation and so on. The affine SIFT (ASIFT) feature is robust to scale, rotation and viewpoint changes but its high computational complexity makes it challenging for ASIFT to be used in applications by a mobile device. To reduce the complexity of ASIFT while maintaining affine invariance, this paper proposes an affine BRISK which replaces SIFT by BRISK of which complexity is much less than SIFT. By fully exploiting the parallelism of affine transformation for viewpoint invariance, this paper achieves the speed up by parallel execution of feature extraction and affine transformation running on the mobile CPU and GPU, respectively. The proposed affine BRISK feature is robust to a viewpoint change with a comparable accuracy. The processing time is 290.64ms on a Galaxy LTE-A mobile.

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T3 - 2016 IEEE International Conference on Consumer Electronics, ICCE 2016

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BT - 2016 IEEE International Conference on Consumer Electronics, ICCE 2016

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Lee C, Rhee CE, Lee HJ. An implementation of an affine BRISK for mobile heterogeneous parallel processors. In Bellido FJ, Diaz-Sanchez D, Vun NCH, Dolar C, Ling W-K, editors, 2016 IEEE International Conference on Consumer Electronics, ICCE 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 435-436. 7430680. (2016 IEEE International Conference on Consumer Electronics, ICCE 2016). https://doi.org/10.1109/ICCE.2016.7430680

An implementation of an affine BRISK for mobile heterogeneous parallel processors

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