• 생물환경조절학회지
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• 제1권1호
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• pp.37-51
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• 1992

Quantizing and extracting visual features of mushroom(Lentinus edodes L.) are crucial to the sorting and grading automation, the growth state measurement, and the dried performance indexing. A computer image processing system was utilized for the extraction and measurement of visual features of front and back sides of the mushroom. The image processing system is composed of the IBM PC compatible 386DK, ITEX PCVISION Plus frame grabber, B/W CCD camera, VGA color graphic monitor, and image output RGB monitor. In this paper, an automatic thresholding algorithm was developed to yield the segmented binary image representing skin states of the front and back sides. An eight directional Freeman's chain coding was modified to solve the edge disconnectivity by gradually expanding the mask size of 3$\times$3 to 9$\times$9. A real scaled geometric quantity of the object was directly extracted from the 8-directional chain element. The external shape of the mushroom was analyzed and converted to the quantitative feature patterns. Efficient algorithms for the extraction of the selected feature patterns and the recognition of the front and back side were developed. The developed algorithms were coded in a menu driven way using MS_C language Ver.6.0, PC VISION PLUS library fuctions, and VGA graphic functions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권2호
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• pp.101-106
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• 2010

We propose an approach to estimate the real-time moving trajectory of an object in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the inputoutput relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

• Nuclear Engineering and Technology
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• 제37권4호
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• pp.395-400
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• 2005

An environmental radon monitoring system, comprising a radon-cup, an etching system, and a track counting system, was constructed. The radon cup is a cylindrical chamber with a radius of 2.2 cm and a height of 3.2 cm in combination with a CR-39 detector. Carbon is impregnated in the bodies of the detector chamber to avoid problem of an electrostatic charge. The optimized etching condition for the CR-39 exposed to a radon environment turned out to be a 6 N NaOH solution at 70^{\circ}$ over a 7hour period. The bulk etch rate under the optimized condition was $1.14{\pm}0.03\;{\mu}m\;h^{-1}$. The diameter of the tracks caused by radon and its progeny were found to be in the range of $10\~25\;{\mu}m$ under the optimized condition. The track images were observed with a track counting system, which consisted of an optical microscope, a color charged couple device (CCD) camera, and an image processor. The calibration factor of this system is obtained to be $0.105{\pm}0.006$ tracks $cm^2$ per Bq $m^{-3}$ d.

• 전자공학회논문지CI
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• 제37권4호
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• pp.37-45
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• 2000

본 논문에서는 관찰자의 위치가 변하더라도 이를 실시간 추적하여 관찰자의 시점에 정확히 대응하는 입체영상을 구현할 수 있는 방법을 제안하였다. 즉 고성능의 하드웨어 장비가 아닌, 일반적으로 사용되는 개인용 컴퓨터에서도 인간의 두 눈을 찾아내고 관찰자의 움직임을 추적할 수 있도록 하는 알고리즘을 제안하였으며, 또한 관찰자의 위치에 따라 달라지는 물체의 모습을 표현해 주기 위해 유한한 다수의 입력 영상 정보를 이용하여 입체 영상을 제작하고 관찰자의 위치 이동에 정확히 대응하는 영상을 디스플레이하는 방법을 제안하였다. 본 논문에서 제안한 방법을 통해 평균 0.39초의 짧은 시간내에 약 97.5%까지 정확히 두 눈의 위치를 찾을 수 있었으며, Fl6 모델을 사용하여 여러 관찰자의 시점에 대응하는 3차원 디스플레이 실험 결과를 보임으로써 본 방법의 우수함을 보였다. 그리고 실제 로봇을 이용하여 좌우 카메라로 얻은 좌우 영상과 인벤터를 통해 버퍼에서 렌더링되는 스테레오 영상과의 유사도를 측정하여, 관찰자의 시점에 대응하여 렌더링되는 3차원 영상이 최적의 시점 대응 영상임을 확인하였다.

• Journal of Biosystems Engineering
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• 제32권3호
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• pp.179-189
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• 2007

Most of the seeds of cucurbitaceous rootstock species used for grafting were mainly sown by hand. This study was carried out to develop an on-line discriminating algorithm of seed direction using machine vision and an automatic seeding system. The seeding system was composed of a supplying device, feeding device, machine vision system, reversing device, seeding device and system control section. Machine vision was composed of a color CCD camera, frame grabber, image inspection chamber, lighting and personal computer. The seed image was segmented into a region of seed part and background part using thresholding technique in which H value of HSI color coordinate system. A seed direction was discriminated by comparing position between the center of circumscribed rectangle to a seed and the center of seed image. It took about 49ms to identify and redirect seed. Line-up status of seed was good the more than 95% of a sowed seed. Seeding capacity of this system was shown to be 10,140 grains per hour, which is three times faster than that of a typical worker.

• Journal of Biosystems Engineering
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• 제36권1호
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• pp.40-47
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• 2011

Image analysis algorithm for the quality evaluation of ginseng seedling was investigated. The images of ginseng seedling were acquired with a color CCD camera and processed with the image analysis methods, such as binary conversion, labeling, and thinning. The processed images were used to calculate the length and weight of ginseng seedlings. The length and weight of the samples could be predicted with standard errors of 0.343 mm, and 0.0214 g respectively, $R^2$ values of 0.8738 and 0.9835 respectively. For the evaluation of the three quality grades of Gab, Eul, and abnormal ginseng seedlings, features from the processed images were extracted. The features combined with the ratio of the lengths and areas of the ginseng seedlings efficiently differentiate the abnormal shapes from the normal ones of the samples. The grade levels were evaluated with an efficient pattern recognition method of support vector machine analysis. The quality grade of ginseng seedling could be evaluated with an accuracy of 95% and 97% for training and validation, respectively. The result indicates that color image analysis with support vector machine algorithm has good potential to be used for the development of an automatic sorting system for ginseng seedling.

• 농업과학연구
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• 제47권4호
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• pp.753-767
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• 2020

The commercial value of strawberries is affected by various factors such as their shape, size and color. Among them, size determined by weight is one of the main factors determining the quality grade of strawberries. In this study, image technology was developed to predict the weight of strawberries using the shape characteristics of strawberry cultivars. For realtime weight measurements of strawberries in transport, an image measurement system was developed for weight prediction with a charge coupled device (CCD) color camera and a conveyor belt. A strawberry weight prediction algorithm was developed for three cultivars, Maehyang, Sulhyang, and Ssanta, using the number of pixels in the pulp portion that measured the strawberry weight. The discrimination accuracy (R2) of the weight prediction models of the Maeyang, Sulhyang and Santa cultivars was 0.9531, 0.951 and 0.9432, respectively. The discriminative accuracy (R2) and measurement error (RMSE) of the integrated weight prediction model of the three cultivars were 0.958 and 1.454 g, respectively. These results show that the 2D imaging technology considering the shape characteristics of strawberries has the potential to predict the weight of strawberries.

• 방송공학회논문지
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• 제21권3호
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• pp.425-435
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• 2016

본 논문에서는 카메라로부터 입력된 영상으로부터 쌀, 커피, 녹차 등 다양한 원료를 양품과 불량품으로 자동 분류하기 위한 분류 모델을 제안한다. 현재 농산물 원료 분류를 위해서 주로 숙달된 노동력의 육안 선택에 의존하고 있지만 작업시간이 길어질수록 반복적인 작업에 의해 분류 능력이 현저히 떨어지는 문제점이 있다. 노동력에 부분적으로 의존하는 기존 제품의 문제점을 해결하기 위해, 본 논문에서는 평균-이동 클러스터링 알고리즘과 단계별 영역 병합 알고리즘을 결합하는 비전기반 자동 원료 분류 알고리즘을 제안한다. 우선 입력 원료 영상에서 평균-이동 클러스터링 알고리즘을 적용하여 영상을 N개의 클러스터 영역으로 분할한다. 다음단계에서 N개의 클러스터 영역 중에서 대표 영역을 선택하고 이웃 영역들의 영역의 색상과 위치 근접성을 기반으로 단계별 영역 병합 알고리즘을 적용하여 유사한 클러스터 영역을 병합한다. 병합된 원료 객체는 RG, GB, BR의 2D 색상 분표로 표현되고, 병합된 원료 객체에 대해 색상 분포 타원을 만든다. 이후 미리 실험적으로 설정된 임계값을 적용하여 원료를 양품과 불량품을 구분한다. 다양한 원료 영상에 대해 본 논문에서 제안하는 알고리즘을 적용한 결과 기존의 클러스터링 알고리즘이나 상업용 분류 방법에 비해 사용자의 인위적 조작이 덜 필요하고 분류성능이 우수한 결과를 나타냄을 알 수 있었다.

• 한국가시화정보학회지
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• 제1권1호
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• pp.98-106
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• 2003

A PLLIF (Planar Liquid Laser Induced Fluorescence) technique has been known to be a useful tool for the measurement of the spray patterns for various spray injectors because it can obtain two-dimensional images with high spatial resolutions without any intrusion on the spray field. In case of dense spray, however, the secondary emission as well as the extinction of an incident laser beam or a fluorescence signal can cause errors in quantifying a mass distribution. Unfortunately, a like-doublet injector which has a dense spray zone at the center may not be a good example or the application of the PLLIF technique. Therefore, we took PLLIF data for the like-doublet injector with a 12 bit color CCD camera by varying laser power, and then assessed their accuracy by comparing with the data obtained with a mechanical patternator and a PDPA (Phase Doppler Particle Analyzer). The experimental results showed that the gray level of fluorescence signal increases nonlinearly due to a secondary emission at the dense spray zone but this nonlinearity can be avoided by reducing the incident laser beam power. In addition, the mass flux distribution of the spray could be obtained by using the mass concentration data from PLLIF technique and the velocity profiles of liquid drops, and this distribution showed good agreement with that of mechanical pattemator. Therefore, it is possible that the PLLIF technique can be successfully applied to finding the mass distributions of like-doublet injectors.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.1243-1253
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• 1993

Visual features of a mushroom(Lentinus Edodes L) are critical in sorting and grading as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. Though actions involved in human grading looks simple, a decision making undereath the simple action comes form the results of the complex neural processing of the visual image. And processing details involved in the visual recognition of the human brain has not been fully investigated yet. Recently, however, an artificial neural network has drawn a great attention because of its functional capability as a partial substitute of the human brain. Since most agricultural products are not uniquely defined in its physical properties and do not have a well defined job structure, a research of the neuro-net based human like information processing toward the agricultural product and processing are widely open and promising. In this pape , neuro-net based grading and sorting system was developed for a mushroom . A computer vision system was utilized for extracting and quantifying the qualitative visual features of sampled mushrooms. The extracted visual features and their corresponding grades were used as input/output pairs for training the neural network and the trained results of the network were presented . The computer vision system used is composed of the IBM PC compatible 386DX, ITEX PFG frame grabber, B/W CCD camera , VGA color graphic monitor , and image output RGB monitor.