• Journal of Sensor Science and Technology
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• v.11 no.5
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• pp.255-262
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• 2002

Human retina is able to detect the edge of an object effectively. We designed a CMOS vision chip by modeling cells of the retina as hardwares involved in edge detection. There are several fluctuation factors which affect characteristics of MOSFETs during CMOS fabrication process and this effect appears as output offset of the vision chip which is composed of pixel arrays and readout circuits. The vision chip detecting edge information from input image is used for input stage of other systems. Therefore, the output offset of a vision chip determine the efficiency of the entire performance of a system. In order to eliminate the offset at the output stage, we designed a vision chip by using CDS(Correlated Double Sampling) technique. Using standard CMOS process, it is possible to integrate with other circuits. Having reliable output characteristics, this chip can be used at the input stage for many applications, like targe tracking system, fingerprint recognition system, human-friendly robot system and etc.

• The Journal of Bigdata
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• v.1 no.1
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• pp.19-22
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• 2016

Many Studies have been actively conducted on the early diagnosis of the crop pest utilizing IT technology. The purpose of the paper is to discuss on the image processing method capable of detecting the crop leaf pest prematurely by analyzing the image of the leaf received from the camera sensor. This paper proposes an algorithm of diagnosing leaf infection by utilizing an improved K means clustering method. Leaf infection grouping test showed that the proposed algorithm illustrated a better performance in the qualitative evaluation.

• Journal of Sensor Science and Technology
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• v.12 no.2
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• pp.57-65
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• 2003

When a vision chip for edge detection using CMOS process is designed, there is a necessity to implement local light adaptive-function for detecting distinctive features of an image at a wide range of light intensities. Local light adaptation is to achive the almost same output level by changing the size of receptive-fields of the local horizontal cell layers according to input light intensities, based on the lateral inhibitive-function of the horizontal cell. Thus, the almost same output level can be obtained whether input light intensities are much or less larger than background. In this paper, the horizontal cells using a resistive network which consists of p-MOSFETs were modeled and analyzed, and the local light adaptive-mechanism of the designed vision chip using the resistive network was verified.

• Journal of Sensor Science and Technology
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• v.26 no.1
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• pp.39-43
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• 2017

Development of vision system as a nondestructive evaluation system can be very useful in screening the defective mechanical parts before they are assembled into the final product. Since the tens of thousands of the mechanical parts are used in an automobile carefully inspecting the quality of the mechanical parts is very important to maximize the performance of the automobile. To sort out the defective mechanical parts before they are assembled, auto parts fabrication companies employ various inspection systems. Nondestructive evaluation systems are getting rapidly popular among various inspection systems. In this study, we have developed a vision system to inspect the inside of the brake caliper, a part that is used to compose a brake which is the most important to the safety of the drivers and the passengers. In a brake caliper, a piston is pushed against the brake disk by oil pressure, causing a friction to damp the rotation of the wheel. Inside the caliper, a groove is positioned to adopt an oil seal to prevent the oil leaks. Inspecting the groove with our vision system, we could examine the existence of the contaminants which are normally the residual tiny pieces from the machining process. We used a high resolution GigE camera, 360 degree lens to look in the inside view of the caliper at once, and a special illumination system in this vision system. We used the edge detection technique to successfully detect the contaminants which were in the form of small metal chips. Labview graphical program was used to process the digital data from the camera and to display the vision and the statistics of the contaminants. We were very successful in detecting the contaminants from the various size calipers. We think we are ready to employ this vision system to the caliper production factories.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.3
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• pp.125-132
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• 2017

Even after applying an image registration, Very High Resolution (VHR) multi-temporal images acquired from different optical satellite sensors such as IKONOS, QuickBird, and Kompsat-2 show a local misalignment due to dissimilarities in sensor properties and acquisition conditions. As the local misalignment, also referred to as Registration Noise (RN), is likely to have a negative impact on multi-temporal information extraction, detecting and reducing the RN can improve the multi-temporal image processing performance. In this paper, an approach to fine registration between VHR multi-temporal images by considering local distribution of RN is proposed. Since the dominant RN mainly exists along boundaries of objects, we use edge information in high frequency regions to identify it. In order to validate the proposed approach, datasets are built from VHR multi-temporal images acquired by optical satellite sensors. Both qualitative and quantitative assessments confirm the effectiveness of the proposed RN-based fine registration approach compared to the manual registration.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.15-33
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• 2021

In this study, the possibility of discriminating Fire blight (FB) infection tested using the hyperspectral imagery. The reflectance of healthy and infected leaves and branches was acquired with 5 nm of full width at high maximum (FWHM) and then it was standardized to 10 nm, 25 nm, 50 nm, and 80 nm of FWHM. The standardized samples were divided into training and test sets at ratios of 7:3, 5:5 and 3:7 to find the optimal bands of FWHM by the decision tree analysis. Classification accuracy was evaluated using overall accuracy (OA) and kappa coefficient (KC). The hyperspectral reflectance of infected leaves and branches was significantly lower than those of healthy green, red-edge (RE) and near infrared (NIR) regions. The bands selected for the first node were generally 750 and 800 nm; these were used to identify the infection of leaves and branches, respectively. The accuracy of the classifier was higher in the 7:3 ratio. Four bands with 50 nm of FWHM (450, 650, 750, and 950 nm) might be reasonable because the difference in the recalculated accuracy between 8 bands with 10 nm of FWHM (440, 580, 640, 660, 680, 710, 730, and 740 nm) and 4 bands was only 1.8% for OA and 4.1% for KC, respectively. Finally, adding two bands (550 nm and 800 nm with 25 nm of FWHM) in four bands with 50 nm of FWHM have been proposed to improve the usability of multispectral image sensors with performing various roles in agriculture as well as detecting FB with other combinations of spectral bands.