• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6C
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• pp.793-800
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• 2004

In this paper, we propose a new automatic pipe grading algorithm for an efficient management of transmission pipe under the ground. Since the conventional transmission pipe evaluation was conducted by subjective decision made by an individual operator, it was difficult to grade them by means of numerical methods and also hard to realistically construct numerical database system. To solve these problems, we Int obtain some information on the current condition of pipes' sections by shooting laser beam at a regular rate and then apply grading algorithm after complete calculation of minimum diameter of pipe. We use some of preprocessing techniques to reduce noise and also use various color models to consider special conditions of each inner pipe. The measurement of pipes' minimum diameter and decision of grade are performed through a detailed processing stages. By some experimental results performed in the field, we show that over 90 percent of correct grade decisions are made by the proposed algorithm.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.357-362
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• 1996

A way of quantitatively describing the tissue architecture we have investigated when developing a computer program for malignancy grading of transitional cell bladder carcinoma. The minimum spanning trees, MST was created by connecting the center points of the nuclei in the tissue section image. These nuclei were found by thresholding the image at an automatically determined threshold followed by a connected component labeling and a watershed algorithm for separation of overlapping nuclei. Clusters were defined in the MST by thresholding the edge lengths. For these clusters geometric and densitometric features were measures. These features were compared by multivariate statistical methods to the subjective grading by the pathologists and the resulting correspondence was 85% on a material of 40 samples.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1230-1242
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• 1993

In a case of mushroom (Lentinus Edodes L.) , visual features are crucial for grading and the quantitative evaluation of the growth state. The extracted quantitative visual features can be used as a performance index for the drying process control or used for the automatic sorting and grading task. First, primary external features of the front and back sides of mushroom were analyzed. And computer vision based algorithm were developed for the extraction and measurement of those features. An automatic thresholding algorithm , which is the combined type of the window extension and maximum depth finding was developed. Freeman's chain coding was modified by gradually expanding the mask size from 3X3 to 9X9 to preserve the boundary connectivity. According to the side of mushroom determined from the automatic recognition algorithm size thickness, overall shape, and skin texture such as pattern, color (lightness) ,membrane state, and crack were quantified and measured. A portion of t e stalk was also identified and automatically removed , while reconstructing a new boundary using the Overhauser curve formulation . Algorithms applied and developed were coded using MS_C language Ver, 6.0, PC VISION Plus library functions, and VGA graphic function as a menu driven way.

• Journal of Biosystems Engineering
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• v.31 no.6 s.119
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• pp.514-523
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• 2006

Production of green pepper has been increased due to customer's preference and a projected ten-year boom in the industry in Korea. This study was carried out to develop an automatic grading and sorting system for green pepper using machine vision. The system consisted of a feeding mechanism, segregation section, an image inspection chamber, image processing section, system control section, grading section, and discharging section. Green peppers were separated and transported using a bowl feeder with a vibrator and a belt conveyor, respectively. Images were taken using color CCD cameras and a color frame grabber. An on-line grading algorithm was developed using Visual C/C++. The green peppers could be graded into four classes by activating air nozzles located at the discharging section. Length and curvature of each green pepper were measured while removing a stem of it. The first derivative of thickness profile was used to remove a stem area of segmented image of the pepper. While pepper is moving at 0.45 m/s, the accuracy of grading sorting for large, medium and small pepper are 86.0%, 81.3% and 90.6% respectively. Sorting performance was 121 kg/hour, and about five times better than manual sorting. The developed system was also economically feasible to grade and sort green peppers showing the cost about 40% lower than that of manual operations.

• Archives of Plastic Surgery
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• v.48 no.3
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• pp.269-277
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• 2021

Background The Sunnybrook facial grading scale is a comprehensive scale for the evaluation of facial paralysis patients. Its results greatly depend on subjective input. This study aimed to develop and validate an automated Sunnybrook facial grading scale (SBface) to more objectively assess disfigurement due to facial paralysis. Methods An application compatible with iOS version 11.0 and up was developed. The software automatically detected facial features in standardized photographs and generated scores following the Sunnybrook facial grading scale. Photographic data from 30 unilateral facial paralysis patients were randomly sampled for validation. Intrarater reliability was tested by conducting two identical tests at a 2-week interval. Interrater reliability was tested between the software and three facial nerve clinicians. Results A beta version of the SBface application was tested. Intrarater reliability showed excellent congruence between the two tests. Moderate to strong positive correlations were found between the software and an otolaryngologist, including the total scores of the three individual software domains and composite scores. However, 74.4% (29/39) of the subdomain items showed low to zero correlation with the human raters (κ<0.2). The correlations between the human raters showed good congruence for most of the total and composite scores, with 10.3% (4/39) of the subdomain items failing to correspond (κ<0.2). Conclusions The SBface application is efficient and accurate for evaluating the degree of facial paralysis based on the Sunnybrook facial grading scale. However, correlations of the software-derived results with those of human raters are limited by the software algorithm and the raters' inconsistency.

• Agricultural and Biosystems Engineering
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• v.1 no.2
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• pp.100-105
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• 2000

Quality sorting of cut flowers is very essential to increase the value of products. There are many factors that determine the quality of cut flowers such as length, thickness, and straightness of stem, and color and maturity of bud. Among these factors, the straightness of stem and the maturity of bud are generally considered to be more difficult to evaluate. A prototype grading and sorting machine for cut flowers was developed and tested for a rose variety. The machine consisted of a chain-drive feed mechanism, a pneumatic discharge system, and a grading system utilizing color image processing and neural network. Artificial neural network algorithm was utilized to grade cut roses based on the straightness of stem and maturity of bud. Test results showed 89% agreement with human expert for the straightness of stem and 90% agreement for the maturity of bud. Average processing time for evaluating straightness of the stem and maturity of the bud were 1.01 and 0.44 second, respectively. Application of neural network eliminated difficulties in determining criteria of each grade category while maintaining similar level of classification error.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.204-204
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• 2004

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1998.12a
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• pp.438-443
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• 1998

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.43-46
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• 1990

Modern society is prevailed a lot of allergies. So, the allergy test is very important. There are many kinds of allergy test. A doctor usually uses skin allergy test among many allergy tests. However, little standadization and objectivity of grading-standard has been established in the skin allergy test. A measurement of the reaction area has been a major objective to perform skin allergy test. Recently, a doctor's method is to measure the reaction area after drawing a line that represents the reaction area on the skin. But this method differs slightly from the real reaction area and individual doctor's measurement is different, because the edge of the reaction area is obscure. In this paper, we propose a algorithm which is able to detect vague edges using the fuzzy set. The algorithm that detects the line and curve is proposed first. Here, the maximum value is calculated by comparing the membership function of the line and curve seperately. We also encode the direction of the line and curve by using 8-direction code. Then, we calculate the reaction area by measuring the pixels which are inside the reaction area. And finally the Allergy grade is decided by grading-standard, and we accomplish faster, the 80re accurate and objective allergy grade decision.

• Journal of Periodontal and Implant Science
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• v.53 no.1
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• pp.38-53
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• 2023

Purpose: The current Classification of Periodontal and Peri-Implant Diseases and Conditions, published and disseminated in 2018, involves some difficulties and causes diagnostic conflicts due to its criteria, especially for inexperienced clinicians. The aim of this study was to design a decision system based on machine learning algorithms by using clinical measurements and radiographic images in order to determine and facilitate the staging and grading of periodontitis. Methods: In the first part of this study, machine learning models were created using the Python programming language based on clinical data from 144 individuals who presented to the Department of Periodontology, Faculty of Dentistry, Süleyman Demirel University. In the second part, panoramic radiographic images were processed and classification was carried out with deep learning algorithms. Results: Using clinical data, the accuracy of staging with the tree algorithm reached 97.2%, while the random forest and k-nearest neighbor algorithms reached 98.6% accuracy. The best staging accuracy for processing panoramic radiographic images was provided by a hybrid network model algorithm combining the proposed ResNet50 architecture and the support vector machine algorithm. For this, the images were preprocessed, and high success was obtained, with a classification accuracy of 88.2% for staging. However, in general, it was observed that the radiographic images provided a low level of success, in terms of accuracy, for modeling the grading of periodontitis. Conclusions: The machine learning-based decision system presented herein can facilitate periodontal diagnoses despite its current limitations. Further studies are planned to optimize the algorithm and improve the results.