• International Journal of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.16-22
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• 2018

This paper presents a solution for personal profiling system based on user-oriented tracking. Here, we introduce a new way to identify and track humans by using two types of cameras: dome and face camera. Dome camera has a wide view angle so that it is suitable for tracking human movement in large area. However, it is difficult to identify a person only by using dome camera because it only sees the target from above. Thus, face camera is employed to obtain facial information for identifying a person. In addition, we also propose a new mechanism to locate human on targeted location by using grid-cell system. These result in a system which has the capability of maintaining human identity and tracking human activity (movement) effectively.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.115-118
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• 2003

This paper describes a system of detecting multi-views faces and estimating their face poses in an omni-directional camera environment for non-intrusive iris recognition. The paper is divided into two parts; First, moving region is identified by using difference-image information. Then this region is analyzed with face-color information to find the face candidate region. Second part is applying PCA (Principal Component Analysis) to detect multi-view faces, to estimate face pose.

• Journal of radiological science and technology
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• v.44 no.1
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• pp.9-14
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• 2021

With the recent advances in radiological science, there was radiographic techniques development and several researches to diagnosing dysphagia. We proposed the new Imaging technology based on Near Infrared radiation (NIR) for video fluoroscopic swallowing study (VFSS). To reduce the risk of the VFSS examination for swallowing rehabilitation, multi-NIR camera system comprised. Based on the multi-NIR camera imaging system, Computational simulation was conducted to identify the potential of the multi-NIR camera imaging system as a clinical tool (screening system). As a result of the simulation applied in this study, the proposed system has a potential to be a clinical solution although there is a few of limitations. we believe that it will be a good tool to support the VFSS as a screening technology in clinical fields.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.1
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• pp.43-50
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• 2013

The narrow dynamic range of most smart phone cameras is severely limited. It usually is narrower than the dynamic range of most scenes. So we proposes a illuminance dynamic range expansion using multi-point knee for smart phone camera. Such as logarithmic functions the proposed method compress the image sensor output signal. Additionally, the proposed method was merged into the gamma that is essential circuit for any cameras. To justifying multi-point knee effectiveness, we configure the control and quality evaluation system for smart phone camera module. Experimental results show that the lost information by cut off and saturated affectively reconstructed in darker and in brighter areas. Finally this methods have advantage to implement without any changing hardware for conventional smart phones.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.2
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• pp.219-226
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• 2017

This paper addresses the effective and quantitative image DB construction for the development of front looking camera systems. The automotive industry has expanded the capability of front camera solutions that will help ADAS(Advanced Driver Assistance System) applications targeting Euro NCAP function requirements. These safety functions include AEB(Autonomous Emergency Braking), TSR(Traffic Signal Recognition), LDW(Lane Departure Warning) and FCW(Forward Collision Warning). In order to guarantee real road safety performance, the driving image DB logged under various real road conditions should be used to train core object classifiers and verify the function performance of the camera system. However, the driving image DB would entail an invalid and time consuming task without proper guidelines. The standard working procedures and design factors required for each step to build an effective image DB for reliable automotive front looking camera systems are proposed.

• Journal of IKEEE
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• v.28 no.3
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• pp.474-478
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• 2024

In this paper, we proposes a deep learning network for quality inspection in a multi-camera inline inspection system for pharmaceutical containers. The proposed deep learning network is specifically designed for pharmaceutical containers by using data produced in real manufacturing environments, leading to more accurate quality inspection. Additionally, the use of an inline-capable deep learning network allows for an increase in inspection speed. The development of the deep learning network for quality inspection in the multi-camera inline inspection system consists of three steps. First, a dataset of approximately 10,000 images is constructed from the production site using one line camera for foreign substance inspection and three area cameras for dimensional inspection. Second, the pharmaceutical container data is preprocessed by designating regions of interest (ROI) in areas where defects are likely to occur, tailored for foreign substance and dimensional inspections. Third, the preprocessed data is used to train the deep learning network. The network improves inference speed by reducing the number of channels and eliminating the use of linear layers, while accuracy is enhanced by applying PReLU and residual learning. This results in the creation of four deep learning modules tailored to the dataset built from the four cameras. The performance of the proposed deep learning network for quality inspection in the multi-camera inline inspection system for pharmaceutical containers was evaluated through experiments conducted by a certified testing agency. The results show that the deep learning modules achieved a classification accuracy of 99.4%, exceeding the world-class level of 95%, and an average classification speed of 0.947 seconds, which is superior to the world-class level of 1 second. Therefore, the effectiveness of the proposed deep learning network for quality inspection in a multi-camera inline inspection system for pharmaceutical containers has been demonstrated.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.478-481
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• 2005

The PMU (Payload Management Unit) is the main subsystem for the management, control and power supply of the MSC (Multi-Spectral Camera) Payload operation. It is the most important function for the electro-optical camera system that performs the Non-Uniformity Correction (NUC) function of the raw imagery data, rearranges the data from the CCD (Charge Coupled Device) detector and output it to the Data Compression and Storage Unit (DCSU). The NUC board in PMU performs it. In this paper, the NUC board system is described in terms of the configuration and the function, the efficiency for non-uniformity correction, and the influence of the data compression upon the peculiar feature of the CCD pixel. The NUC board is an image-processing unit within the PMU that receives video data from the CEV (Camera Electronic Unit) boards via a hotlinkand performs non-uniformity corrections upon the pixels according to commands received from the SBC (Single Board Computer) in the PMU. The lossy compression in DCSU needs the NUC in on-orbit condition.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.631-632
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• 2006

The assembly of camera lens modules fur the mobile phone has not been automated so far. They are still assembled manually because of high precision of all parts and hard-to-recognize lens by vision camera. In addition, the very short life cycle of the camera phone lens requires flexible and intelligent automation. This study proposes a fast and accurate identification system of the parts by distributing the camera for 4 degree of freedom assembly robot system. Single or multi-cameras can be installed according to the part's image capture and processing mode. It has an agile structure which enables adaptation with the minimal job change. The framework is proposed and the experimental result is shown to prove the effectiveness.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.12
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• pp.1209-1216
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• 2009

This paper presents the pose estimation of a small UAV utilizing visual information from low cost cameras installed indoor. To overcome the limitation of the outside flight experiment, the indoor flight test environment based on multi-camera systems is proposed. Computer vision algorithms for the proposed system include camera calibration, color marker detection, and pose estimation. The well-known extended Kalman filter is used to obtain an accurate position and pose estimation for the small UAV. This paper finishes with several experiment results illustrating the performance and properties of the proposed vision-based indoor flight test environment.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.280-287
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• 2001

The studies to estimate the surface spectral reflectance of an object have received widespread attention using the multi-spectral camera system. However, the multi-spectral camera system requires the additional color filter according to increment of the channel and system complexity is increased by multiple capture. Thus, this paper proposes an algorithm to reduce the estimation error of surface spectral reflectance with the conventional 3-band RGB camera. In the proposed method, adaptive principal components for each pixel are calculated by renewing the population of surface reflectances and the adaptive principal components can reduce estimation error of surface spectral reflectance of current pixel. To evacuate performance of the proposed estimation method, 3-band principal component analysis, 5-band wiener estimation method, and the proposed method are compared in the estimation experiment with the Macbeth ColorChecker. As a result, the proposed method showed a lower mean square ems between the estimated and the measured spectra compared to the conventional 3-band principal component analysis method and represented a similar or advanced estimation performance compared to the 5-band wiener method.