• Journal of Sensor Science and Technology
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• v.28 no.2
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• pp.94-100
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• 2019

Drivable area detection is a major task in advanced driver assistance systems. For drivable area detection, several studies have proposed vision-sensor-based approaches. However, conventional drivable area detection methods that use vision sensors are not suitable for environments with changes in road elevation. In addition, if the boundary between the road and vegetation is not clear, judging a vegetation area as a drivable area becomes a problem. Therefore, this study proposes an accurate method of detecting drivable areas in environments in which road elevations change and vegetation exists. Experimental results show that when compared to the conventional method, the proposed method improves the average accuracy and recall of drivable area detection on the KITTI vision benchmark suite by 3.42%p and 8.37%p, respectively. In addition, when the proposed vegetation area removal method is applied, the average accuracy and recall are further improved by 6.43%p and 9.68%p, respectively.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.431-433
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• 2006

The goal of this paper is improvement of vision inspection accuracy by using histogram specification operation. The histogram is composed of horizontal axis of image intensity value and vertical axis of pixel number in image. In appearance vision inspection, the histogram of reference image and input image are different because of minutely lighting distinction. The minutely lighting distinction is main reason of vision inspection error in many cases. Therefore we made an effort for elevation of vision inspection accuracy by making the identical histogram of reference image and input image. As a result of this area separation histogram specification algorithm, we could increase the exactness of vision inspection and prevent system error from physical and spirit condition of human. Also this system has been developed only using PC, CCD Camera and Visual C++ for universal workplace.

• Journal of Korean Ophthalmic Optics Society
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• v.12 no.3
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• pp.89-91
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• 2007

Color vision test was conducted to elementary school students at age of 9 to 13, with total subject number of 598 which live in Gwang Ju area. 325 (54.3%) boys and 273 (45.7%) girls were subjected using Hahn Color Vision test to find out color recognition problems. 1. Ratios of color vision abnomality were 6.25% in 9 year old students, 9.2% in 10 year old students, 8.4% in 12 year old students, 7.8% in 13 year old students, which means 7.9% of the total 598 subjects had color recognition matters. 2. Red-Green dyschromatopsia was 7.8% of the total 598 subjects which includes most of subjects. None of them had green-yellow recognition problem. Full dyschromatopsia had frequency of 0.2%. 3. 10.7% of boys had color vision abnormality, while 10.7% of girls had color vision abnormality, which means that boys have color recognition problems with higher rate than girls.

• Journal of Biosystems Engineering
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• v.33 no.6
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• pp.446-452
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• 2008

Machine vision system was used for analyzing leaf color disorders of tomato plants in a greenhouse. From the day when a few leave of tomato plants had started to wither, a series of images were captured by 4 times during 14 days. Among several color image spaces, Saturation frame in HSI color space was adequate to eliminate a background and Hue frame was good to detect infected disease area and tomato fruits. The processed image ($G{\sqcup}b^*$ image) by OR operation between G frame in RGB color space and $b^*$ frame in $La^*b^*$ color space was useful for image segmentation of a plant canopy area. This study calculated a ratio of the infected area to the plant canopy and manually analyzed leaf color disorders through an image segmentation for Hue frame of a tomato plant image. For automatically analyzing plant leave disease, this study selected twenty-seven color patches on the calibration bars as the corresponding to leaf color disorders. These selected color patches could represent 97% of the infected area analyzed by the manual method. Using only ten color patches among twenty-seven ones could represent over 85% of the infected area. This paper showed a proposed machine vision system may be effective for evaluating various leaf color disorders of plants growing in a greenhouse.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.641-643
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• 2006

Add-Vision has developed a low-cost print technology for P-OLED displays on flexible substrates that meets several essentials for a new technology including: (1) Functionality including low DC voltage and wide color gamut; (2) Utilization of inexpensive tools; (3) Performance matching entry applications and markets. AVI's process is based on large-area printing of a combination of doped emissive and air-stable cathode inks utilizing truly low-cost tools to create printed P-OLEDs.

• Journal of Biosystems Engineering
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• v.29 no.3
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• pp.275-280
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• 2004

The main objective of this study was to develop and evaluate a model for estimating pigs weight using computer vision for improving the management in Korean swine farms in Korea. This research was carried out in two steps: 1) to find a model that relates the projection area with the weight of a pig; 2) to implement the model in a computer vision system mainly consisted of a monochrome CCD camera, a frame grabber and a computer system for estimating the weight of pigs in a non-contact, real-time manner. The model was developed under an important assumption there were no observable genetic differences among the pigs. The main results were: 1) The relationship between the projection area and the weight of pigs was W = 0.0569 ${\times}$ A - 32.585($R^2$ = 0.953), where W is the weight in kg; A is the projection area of a pig in $\textrm{cm}^2$; 2) The model could estimate the weight of pigs with an error less than 3.5%.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.2 s.21
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• pp.14-21
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• 2005

This paper describes the system for detecting the tank crew's(loader's) hand, arm, head and the upper half of the body in a danger area between the turret ceiling and the upper breech mechanism by computer vision-based method. This system informs danger of pressed to death to gunner and commander for the safety of operating mission. The camera mounted ort the top portion of the turret ceiling. The system sets search moving object from this image and detects by using change of image, laplacian operator and clustering algorithm in this area. It alarms the tank crews when it's judged that dangerous situation for operating mission. The result In this experiment shows that the detection rate maintains in $81{\sim}98$ percents.

• Smart Media Journal
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• v.12 no.9
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• pp.28-37
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• 2023

The main factor affecting the decline in rice yield is damage caused by localized heavy rains or typhoons. The method of analyzing the rice lodging area is difficult to obtain objective results based on visual inspection and judgment based on field surveys visiting the affected area. it requires a lot of time and money. In this paper, we propose the method of estimation and diagnosis for rice lodging areas using a Vision Transformer-based Segformer for RGB images, which are captured by unmanned aerial vehicles. The proposed method estimates the lodging, normal, and background area using the Segformer model, and the lodging rate is diagnosed through the rice field inspection criteria in the seed industry Act. The diagnosis result can be used to find the distribution of the rice lodging areas, to show the trend of lodging, and to use the quality management of certified seed in government. The proposed method of rice lodging area estimation shows 98.33% of mean accuracy and 96.79% of mIoU.

• Journal of Korean Ophthalmic Optics Society
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• v.10 no.4
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• pp.329-337
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• 2005

This study investigated prevalence of color vision loss different from children's residence. The study subjects are 3 to 6 grade's children of elementary school in four areas. The blood lead and urinary mercury were analysed using Atomic Absorption Spectroscopy. All of participations' blood lead and urine mercury concentration were below suggested level of concern such as criteria by CDC and ATSDR. Color vision was assessed by the Lanthony D-15 desaturated panel. Color vision loss was quantitatively established by the Color Confusion Index (CCI) and qualitatively classified by type of acquired dyschromatopsia according to Verriest's classification. The prevalence of color vision loss and CCI value for children in industrial area was significantly higher than other areas(p<0.05). However blood lead and urinary mercury concentration level was not correlated to the color vision loss. Therefore we believed that other environmental neurotoxic substance except metal had an effects on color vision loss for children in industrial area.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.116.4-116
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• 2002

$\textbullet$In case of using overhead camera system, mobile robot moves under static working area. $\textbullet$Mobile robot must use onboard camera system to work under wide working area. $\textbullet$Mobile robot must have wireless LAN to remove restriction of movement. $\textbullet$Onboard camera system must have wireless LAN environment. $\textbullet$We develop embedded vision platform using onboard camera.