Cheol-Ho Choi

2024

1. Sensors

   Contrast Enhancement Method using Region-based Dynamic Clipping Technique for LWIR-based Thermal Camera of Night Vision Systems

   Cheol-Ho Choi, Joonhwan Han, Jeongwoo Cha, Hyunmin Choi, Jungho Shin, Taehyun Kim, and Hyun Woo Oh

   Sensors, Jun 2024

   Abstract BibTeX HTML PDF

   In the autonomous driving industry, there is a growing trend to employ long-wave infrared (LWIR)-based uncooled thermal-imaging cameras, capable of robustly collecting data even in extreme environments. Consequently, both industry and academia are actively researching contrast-enhancement techniques to improve the quality of LWIR-based thermal-imaging cameras. However, most research results only showcase experimental outcomes using mass-produced products that already incorporate contrast-enhancement techniques. Put differently, there is a lack of experimental data on contrast enhancement post-non-uniformity (NUC) and temperature compensation (TC) processes, which generate the images seen in the final products. To bridge this gap, we propose a histogram equalization (HE)-based contrast enhancement method that incorporates a region-based clipping technique. Furthermore, we present experimental results on the images obtained after applying NUC and TC processes. We simultaneously conducted visual and qualitative performance evaluations on images acquired after NUC and TC processes. In the visual evaluation, it was confirmed that the proposed method improves image clarity and contrast ratio compared to conventional HE-based methods, even in challenging driving scenarios such as tunnels. In the qualitative evaluation, the proposed method demonstrated upper-middle-class rankings in both image quality and processing speed metrics. Therefore, our proposed method proves to be effective for the essential contrast enhancement process in LWIR-based uncooled thermal-imaging cameras intended for autonomous driving platforms.

   @article{2024_SENSORS,
     title = {Contrast Enhancement Method using Region-based Dynamic Clipping Technique for LWIR-based Thermal Camera of Night Vision Systems},
     language = {en},
     journal = {Sensors},
     publisher = {MDPI},
     author = {Choi, Cheol-Ho and Han, Joonhwan and Cha, Jeongwoo and Choi, Hyunmin and Shin, Jungho and Kim, Taehyun and Oh, Hyun Woo},
     month = jun,
     year = {2024},
     volume = {24},
     number = {12},
   }

2023

1. Access

   Cell-Based Refinement Processor Utilizing Disparity Characteristics of Road Environment for SGM-Based Stereo Vision Systems

   Cheol-Ho Choi, Hyun Woo Oh, Joonhwan Han, and Jungho Shin

   IEEE Access, Dec 2023, pp. 138122–138140

   Extended from Published and Presented Paper on DSD’2023

   Abstract BibTeX HTML PDF

   Embedded stereo vision systems based on traditional approaches often require a disparity refinement process to enhance image quality. Weighted median filter (WMF)-based processors are commonly employed for their excellent refinement performance. However, when implemented on a field-programmable gate array (FPGA), WMF-based processors face a trade-off between hardware resource utilization and refinement performance. To address this trade-off, we previously proposed a new disparity refinement processor based on the hybrid max-median filter (HMMF). However, our earlier work did not guarantee flawless operation in large occluded and texture-less regions, particularly in areas with numerous holes. In order to overcome this limitation of conventional processors, we proposed a cell-based disparity refinement processor. This processor extends our previous HMMF-based disparity refinement processor. To evaluate its refinement performance, we conducted experiments using four types of publicly available stereo datasets. When comparing refinement performance, our proposed processor outperforms conventional processors when using the KITTI 2012 and 2015 stereo benchmark datasets. Additionally, the results demonstrate that our proposed processor exhibits superior refinement performance when applied to the Cityscapes and StereoDriving datasets in comparison to conventional processors. Furthermore, when considering hardware resource utilization, our proposed processor demonstrates lower resource requirements than conventional processors when implemented on an FPGA. Therefore, our proposed disparity refinement processor is well-suited for the disparity refinement process in stereo vision systems that require cost-effectiveness and high performance.

   @article{2023_ACCESS,
     title = {Cell-Based Refinement Processor Utilizing Disparity Characteristics of Road Environment for SGM-Based Stereo Vision Systems},
     volume = {11},
     issn = {2169-3536},
     doi = {10.1109/ACCESS.2023.3338649},
     language = {en},
     journal = {IEEE Access},
     author = {Choi, Cheol-Ho and Oh, Hyun Woo and Han, Joonhwan and Shin, Jungho},
     month = dec,
     year = {2023},
     pages = {138122--138140},
   }

2022

1. HCIS

   Face Detection using Haar Cascade Classifiers based on Vertical Component Calibration

   Cheol-Ho Choi, Junghwan Kim, Jongkil Hyun, Younghyeon Kim, and Byungin Moon

   Human-centric Computing and Information Sciences (HCIS), Mar 2022, pp. 1–17

   Abstract BibTeX HTML PDF

   The growing significance of the security and human management fields attracts active research related to face detection and recognition systems. Among these face detection techniques based on machine learning, Haar cascade classifiers are widely used because of their high accuracy for human frontal faces. However, the Haar cascade classifiers have a limitation in that the processing time increases as the number of false positives increases because they detect human faces based on the sub-window operation. Therefore, in this paper, a preprocessing method based on a 2D Haar discrete wavelet transform is proposed for face detection. The proposed method improves the processing speed by reducing the number of false positives through a vertical component calibration process using the vertical and horizontal components. The results of the face detection experiments that use a public test dataset comprising 2,845 images showed that the proposed method improved the processing speed by 32.05% and reduced the number of false positives by 25.46%, compared with those of the histogram equalization that shows the best performance case among conventional filter-based pre-processing methods. In addition, the performance of the proposed method is similar to those of conventional image contraction-based methods. In an experiment using a private dataset, the proposed method showed a 53.85% reduction in the total number of false positives compared with that of the Gaussian filter while maintaining the total number of true positives. The F1 score of the proposed method shows a 1.39% improvement compared with those of Lanczos-3 that shows the best performance case.

   @article{2022_HCIS,
     title = {Face Detection using Haar Cascade Classifiers based on Vertical Component Calibration},
     issn = {2192-1962},
     author = {Choi, Cheol-Ho and Kim, Junghwan and Hyun, Jongkil and Kim, Younghyeon and Moon, Byungin},
     journal = {Human-centric Computing and Information Sciences (HCIS)},
     volume = {12},
     number = {11},
     month = mar,
     year = {2022},
     pages = {1--17},
   }

2019

1. MOTL

   Heartbeat Detection using a Doppler Radar Sensor based on the Scaling Function of Wavelet Transform

   Cheol-Ho Choi, Jae-Hyun Park, Ha-Neul Lee, and Jong-Ryul Yang

   Microwave and Optical Technology Letters (MOTL), Jul 2019, pp. 1792–1796

   Abstract BibTeX HTML PDF

   The heartbeat detection using the scaling function of Wavelet transform is proposed for a Doppler radar sensor. The conventional methods such as the fast-Fourier transform and the autocorrelation show the respiration rate and the heartbeat from the raw data of the radar sensors acquiring for a sufficient sampling time. The methods have the limit to detect the biometric information that varies with real-time because they only show the overall statistical information of the sampled data. In the proposed method, the scaling function in the Daubechies wavelet transform can be used to accurately find out the periodicity of radar signals for detecting heartbeat varying in real-time. The results of the signal processing using the radar signals acquired for 3 min results show that the proposed method lowered a mean error rate of 2.5% and a SD of 2.0% compared with the method using the wavelet function. The proposed method in the measurement for 1 minute using the radar sensor also showed the lowest mean error rate of 3.8% and the low SD of 3.2% using the contact sensor as the reference among various signal processing methods including auto-correlation and peak detection with filtering.

   @article{2019_MOTL,
     title = {Heartbeat Detection using a Doppler Radar Sensor based on the Scaling Function of Wavelet Transform},
     author = {Choi, Cheol-Ho and Park, Jae-Hyun and Lee, Ha-Neul and Yang, Jong-Ryul},
     journal = {Microwave and Optical Technology Letters (MOTL)},
     volume = {61},
     number = {7},
     pages = {1792--1796},
     month = jul,
     year = {2019},
     publisher = {Wiley Online Library},
   }