A DPM based object detector using HOG-LBP features

Tanase Cucliciu; Chih Yang Lin; Kahlil Muchtar

doi:10.1109/ICCE-China.2017.7991122

A DPM based object detector using HOG-LBP features

Tanase Cucliciu, Chih Yang Lin, Kahlil Muchtar

機械工程學系

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

7 引文斯高帕斯（Scopus）

摘要

In this paper, we combine Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) in order to detect the cranium and its components; namely, the brain, eyes and mouth. Furthermore, Deformable Part Model (DPM) algorithm is paired with the AdaBoost for training and classification. We use a CT/PET database acquired from the National Biomedical Imaging Archive (NBIA) in order to train and test our solution.

原文	???core.languages.en_GB???
主出版物標題	2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	315-316
頁數	2
ISBN（電子）	9781509040179
DOIs	https://doi.org/10.1109/ICCE-China.2017.7991122
出版狀態	已出版 - 25 7月 2017
事件	4th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017 - Taipei, United States 持續時間: 12 6月 2017 → 14 6月 2017

出版系列

名字	2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	4th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017
國家/地區	United States
城市	Taipei
期間	12/06/17 → 14/06/17

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10.1109/ICCE-China.2017.7991122

引用此

Cucliciu, T., Lin, C. Y., & Muchtar, K. (2017). A DPM based object detector using HOG-LBP features. 於 2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017 (頁 315-316). 文章 7991122 (2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-China.2017.7991122

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title = "A DPM based object detector using HOG-LBP features",

abstract = "In this paper, we combine Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) in order to detect the cranium and its components; namely, the brain, eyes and mouth. Furthermore, Deformable Part Model (DPM) algorithm is paired with the AdaBoost for training and classification. We use a CT/PET database acquired from the National Biomedical Imaging Archive (NBIA) in order to train and test our solution.",

author = "Tanase Cucliciu and Lin, {Chih Yang} and Kahlil Muchtar",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 4th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017 ; Conference date: 12-06-2017 Through 14-06-2017",

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Cucliciu, T, Lin, CY & Muchtar, K 2017, A DPM based object detector using HOG-LBP features. 於 2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017., 7991122, 2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017, Institute of Electrical and Electronics Engineers Inc., 頁 315-316, 4th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017, Taipei, United States, 12/06/17. https://doi.org/10.1109/ICCE-China.2017.7991122

A DPM based object detector using HOG-LBP features. / Cucliciu, Tanase; Lin, Chih Yang; Muchtar, Kahlil.
2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 315-316 7991122 (2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017).

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

TY - GEN

T1 - A DPM based object detector using HOG-LBP features

AU - Cucliciu, Tanase

AU - Lin, Chih Yang

AU - Muchtar, Kahlil

PY - 2017/7/25

Y1 - 2017/7/25

N2 - In this paper, we combine Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) in order to detect the cranium and its components; namely, the brain, eyes and mouth. Furthermore, Deformable Part Model (DPM) algorithm is paired with the AdaBoost for training and classification. We use a CT/PET database acquired from the National Biomedical Imaging Archive (NBIA) in order to train and test our solution.

AB - In this paper, we combine Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) in order to detect the cranium and its components; namely, the brain, eyes and mouth. Furthermore, Deformable Part Model (DPM) algorithm is paired with the AdaBoost for training and classification. We use a CT/PET database acquired from the National Biomedical Imaging Archive (NBIA) in order to train and test our solution.

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DO - 10.1109/ICCE-China.2017.7991122

M3 - 會議論文篇章

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T3 - 2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017

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BT - 2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017

Y2 - 12 June 2017 through 14 June 2017

ER -

Cucliciu T, Lin CY, Muchtar K. A DPM based object detector using HOG-LBP features. 於 2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 315-316. 7991122. (2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017). doi: 10.1109/ICCE-China.2017.7991122

A DPM based object detector using HOG-LBP features

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