Loading [MathJax]/extensions/MathMenu.js
H. Seckin Demir - IEEE Xplore Author Profile
Access provided by:

MIT Libraries

Sign Out
Access provided by:

MIT Libraries

Sign Out
ADVANCED SEARCH

Author details

< Back

H. Seckin Demir

Also published under: H. Seçkin Demir

Publications
6
Citations
146
Publications by Year
20152020
Co-Authors:
Show All Co-Authors (72)
Image of author H. Seckin Demir

H. Seckin Demir

Also published under: H. Seçkin Demir

Affiliation

Department of Electrical
Computer
and Energy Engineering, Arizona State University, Tempe, AZ, USA

Publication Topics

Biography

H. Seckin Demir received the B.S. and M.S. degrees in electrical and electronics engineering from Bilkent University, Ankara, Turkey, in 2014 and 2017, respectively. He is currently pursuing the Ph.D. degree with the Electrical, Computer, and Energy Engineering Department, Arizona State University, Tempe, AZ, USA.,From 2014 to 2019, he worked as a Computer Vision Scientist with ASELSAN Inc., Ankara. He is also a Graduate Research Assistant with Electrical, Computer, and Energy Engineering Department, Arizona State University. His research interests include computer vision, image processing, and machine learning.(Based on document published on 2 October 2020).
Publications
6
Citations
146
Publications by Year
20152020
Co-Authors:
Show All Co-Authors (72)
Author's Published Works

Showing 1-6 of 6 results

  • Sort

Filter Results

Show

Sort By

Results

Energy-Efficient Image Recognition System for Marine Life

H. Seckin Demir;Jennifer Blain Christen;Sule Ozev

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year: 2020 | Volume: 39, Issue: 11 | Journal Article |
Cited by: Papers (16)
This article focuses on designing an energy-efficient image recognition system for marine monitoring. One of the main challenges of an underwater imaging system is the strict power consumption constraints due to the limited on-site resources. Considering the need for continuous operation in different water turbidity levels and background illumination conditions, an energy-efficient approach is nee...Show More

Energy-Efficient Image Recognition System for Marine Life

H. Seckin Demir;Jennifer Blain Christen;Sule Ozev

Year: 2020 | Volume: 39, Issue: 11 | Journal Article |

NTIRE 2018 Challenge on Image Dehazing: Methods and Results

Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (87)
This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE data...Show More

NTIRE 2018 Challenge on Image Dehazing: Methods and Results

Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen

Year: 2018 | Conference Paper |

Co-difference based object tracking algorithm for infrared videos

H. Seckin Demir;A. Enis Cetin

2016 IEEE International Conference on Image Processing (ICIP)
Year: 2016 | Conference Paper |
Cited by: Papers (12)
This paper presents a novel infrared (IR) object tracking algorithm based on the co-difference matrix. Extraction of co-difference features is similar to the well known covariance method except that the vector product operator is redefined in a multiplication-free manner. The new operator yields a computationally efficient implementation for real time object tracking applications. Experiments on a...Show More

Co-difference based object tracking algorithm for infrared videos

H. Seckin Demir;A. Enis Cetin

Year: 2016 | Conference Paper |

Comparison of infrared and visible imagery for object tracking: Toward trackers with superior IR performance

Erhan Gundogdu;Huseyin Ozkan;H. Seckin Demir;Hamza Ergezer;Erdem Akagündüz;S. Kubilay Pakin

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2015 | Conference Paper |
The subject of this paper is the visual object tracking in infrared (IR) videos. Our contribution is twofold. First, the performance behaviour of the state-of-the-art trackers is investigated via a comparative study using IR-visible band video conjugates, i.e., video pairs captured observing the same scene simultaneously, to identify the IR specific challenges. Second, we propose a novel ensemble ...Show More

Comparison of infrared and visible imagery for object tracking: Toward trackers with superior IR performance

Erhan Gundogdu;Huseyin Ozkan;H. Seckin Demir;Hamza Ergezer;Erdem Akagündüz;S. Kubilay Pakin

Year: 2015 | Conference Paper |

A comparison of focus measures for autofocus systems in IR cameras

H. Seçkin Demir

2015 23nd Signal Processing and Communications Applications Conference (SIU)
Year: 2015 | Conference Paper |
In this study, focus measures used in autofocus systems are compared on a newly constructed IR image dataset. The dataset contains images from different scenes and scenerios taken by 2 IR cameras that have different characteristics. A comparison is made on this dataset based on the accuracy, computation time and noise robustness.Show More

A comparison of focus measures for autofocus systems in IR cameras

H. Seçkin Demir

Year: 2015 | Conference Paper |

Vessel classification on UAVs using inertial data and IR imagery

H. Seçkin Demir;Erdem Akagündüz;S. Kubilay Pakin

2015 23nd Signal Processing and Communications Applications Conference (SIU)
Year: 2015 | Conference Paper |
In this study, a civilian ship dataset is constructed via images captured by an infrared camera on an unmanned flying vehicle. By using this dataset and synchronized inertial data (UAV altitude and orientation, gimbal angles), a vessel classification method is proposed. The method first calculates the ship base length in meters by using segmented ship image and inertial data. By fusing the descrip...Show More

Vessel classification on UAVs using inertial data and IR imagery

H. Seçkin Demir;Erdem Akagündüz;S. Kubilay Pakin

Year: 2015 | Conference Paper |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.