• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.8-15
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• 2003

In Korea, quality evaluation of dried oak mushrooms are done first by classifying them into more than 10 different categories based on the state of opening of the cap, surface pattern, and colors. And mushrooms of each category are further classified into 3 or 4 groups based on its shape and size, resulting into total 30 to 40 different grades. Quality evaluation and sorting based on the external visual features are usually done manually. Since visual features of mushroom affecting quality grades are distributed over the entire surface of the mushroom, both front (cap) and back (stem and gill) surfaces should be inspected thoroughly. In fact, it is almost impossible for human to inspect every mushroom, especially when they are fed continuously via conveyor. In this paper, considering real time on-line system implementation, image processing algorithms utilizing artificial neural network have been developed for the quality grading of a mushroom. The neural network based image processing utilized the raw gray value image of fed mushrooms captured by the camera without any complex image processing such as feature enhancement and extraction to identify the feeding state and to grade the quality of a mushroom. Developed algorithms were implemented to the prototype on-line grading and sorting system. The prototype was developed to simplify the system requirement and the overall mechanism. The system was composed of automatic devices for mushroom feeding and handling, a set of computer vision system with lighting chamber, one chip microprocessor based controller, and pneumatic actuators. The proposed grading scheme was tested using the prototype. Network training for the feeding state recognition and grading was done using static images. 200 samples (20 grade levels and 10 per each grade) were used for training. 300 samples (20 grade levels and 15 per each grade) were used to validate the trained network. By changing orientation of each sample, 600 data sets were made for the test and the trained network showed around 91 % of the grading accuracy. Though image processing itself required approximately less than 0.3 second depending on a mushroom, because of the actuating device and control response, average 0.6 to 0.7 second was required for grading and sorting of a mushroom resulting into the processing capability of 5,000/hr to 6,000/hr.

• Journal of the Optical Society of Korea
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• v.17 no.2
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• pp.188-199
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• 2013

This paper presents a novel algorithm for nighttime detection of the lane markers painted on a road at night. First of all, the proposed algorithm uses neighborhood average filtering, 8-directional Sobel operator and thresholding segmentation based on OTSU's to handle raw lane images taken from a digital CCD camera. Secondly, combining intensity map and gradient map, we analyze the distribution features of pixels on boundaries of lanes in the nighttime and construct 4 feature sets for these points, which are helpful to supply with sufficient data related to lane boundaries to detect lane markers much more robustly. Then, the searching method in multiple directions- horizontal, vertical and diagonal directions, is conducted to eliminate the noise points on lane boundaries. Adapted Hough transformation is utilized to obtain the feature parameters related to the lane edge. The proposed algorithm can not only significantly improve detection performance for the lane marker, but it requires less computational power. Finally, the algorithm is proved to be reliable and robust in lane detection in a nighttime scenario.

• Corrosion Science and Technology
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• v.18 no.6
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• pp.253-257
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• 2019

A deep learning based real-time painting surface inspection algorithm is proposed herein, designed for developing an autonomous inspection drone. The painting surface inspection is usually conducted manually. However, the manual inspection has a limitation in obtaining accurate data for correct judgement on the surface because of human error and deviation of individual inspection experiences. The best method to replace manual surface inspection is the vision-based inspection method with a camera, using various image processing algorithms. Nevertheless, the visual inspection is difficult to apply to surface inspection due to diverse appearances of material, hue, and lightning effects. To overcome technical limitations, a deep learning-based pattern recognition algorithm is proposed, which is specialized for painting surface inspections. The proposed algorithm functions in real time on the embedded board mounted on an autonomous inspection drone. The inspection results data are stored in the database and used for training the deep learning algorithm to improve performance. The various experiments for pre-inspection of painting processes are performed to verify real-time performance of the proposed deep learning algorithm.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.121-128
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• 2010

K-means clustering is an iterative algorithm in which items are moved among sets of clusters until the desired set is reached. K-means clustering has been widely used in many applications, such as market research, pattern analysis or recognition, image processing, etc. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters that we want, because it is more primitive, explorative. In this paper we propose a new method of k-means clustering using a center of gravity for grid-based sample. It is more fast than any traditional clustering method and maintains its accuracy.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.8-12
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• 1999

Since the advent of high resolution satellite image, possibilities of applying various human interpretation mechanism to these images have increased. Also many studies about these possibilities in many fields such as computer vision, pattern recognition, artificial intellegence and remote sensing have been done. In this field of these studies, texture is defined as a kind of quantity related to spatial distribution of brightness and tone and also plays an important role for interpretation of images. Especially, methods of obtaining texture by statistical model have been studied intensively. Among these methods, texture measurement method based on cooccurrence matrix is highly estimated because it is easy to calculate texture features compared with other methods. In addition, these results in high classification accuracy when this is applied to satellite images and aerial photos. But in the existing studies using cooccurrence matrix, features have been chosen arbitrarily without considering feature variation. And not enough studies have been implemented for appropriate resolution selection in which cooccurrence matrix can extract texture. Therefore, this study reviews the concept of cooccurrence matrix as a texture measurement method, evaluates usefulness of several features obtained from cooccurrence matrix, and proposes appropriate resolution by investigating variance trend of several features.

• Journal of Biomedical Engineering Research
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• v.12 no.2
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• pp.99-106
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• 1991

For the automatic detection of pulmonary blood vessels, a new algorithm is proposed using the fact that human recognizes a pattern orderly according to their size. This method simulates the human recognition process by the pyramid images. For the detection of vessels using multilevel image, large and wtde ones are detected from the most compressed level, followed by the detection of small and narrow ones from the less compressed images with FCM(fuzzy c means) clustering algorithm which classifies similar data into a group. As the proposed algorithm detects blood vessels orderly according to their size, there is no need to consider the variation of parameters and the branch points which should be considered in other detection algirithms. In the detection of patterns whose size changes successively like pulmonary blood vessels, this proposed algorithm can be properly applied

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.682-687
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• 2005

Behavioral sequences of the medaka(Oryzias latipes) were investigated through an image system in response to medaka treated with the insecticide and medaka not treated with the insecticide, diazinon(0.1 mg/1). After much observation, behavioral patterns could be divided into 4 patterns: active smooth, active shaking, inactive smooth, and inactive shaking. These patterns were analyzed by 5 features: speed ratio, x and y axes projection, FFT to angle transition, fractal dimension, and center of mass. Each pattern was classified using decision tree. It provide a natural way to incorporate prior knowledge from human experts in fish behavior, The main focus of this study was to determine whether the decision tree could be useful in interpreting and classifying behavior patterns of the animal.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2008.02a
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• pp.187-190
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• 2008

디지털 영상기기의 발달과 함께 디지털 영상은 우리의 생활의 일부가 되었다. 이러한 디지털 영상이 생활의 일부가 되면서 범죄자들은 범죄현장의 흔적을 디지털 영상으로 저장하기도 하며, 디지털 영상으로 저장된 CCTV를 통하여 범죄의 현장을 재현하기도 한다[5][6]. 이러한 디지털 영상은 사건을 해결하는데 중요한 역할을 하기에 사건을 수사하는데 있어 수집하여야 할 중요한 대상중의 하나이다. 이러한 디지털 영상은 하나의 분석대상에 다수의 파일로 존재할 수 있으며, 이러한 다수의 디지털 영상 속에서 사건 해결에 도움을 줄 수 있는 중요한 영상을 구분하기 위해서는 많은 시간과 인력이 필요하다. 따라서 다수의 영상 증거물을 디지털 영상 포렌식의 한 분야인 디지털 영상 분류기법을 통하여 수사의 편리함과 정확성을 향상시켜야 할 필요가 있다. 하지만 디지털 영상의 수사를 돕기 위한 디지털 영상의 분류기법은 디지털 영상 포렌식의 영역 중에서 연구가 이루어져 있지 않은 영역중의 하나이며, 연구가 진행되고 있는 방식도 국내가 아닌 국외의 법과 수사 환경을 중심으로 연구되어지고 있다. 따라서 국내의 법과 수사 환경에 적용할 수 있는 디지털 영상 분류기법에 대하여 연구할 필요성이 있다. 이에 대해 본 논문에서는 디지털 영상 포렌식 중 하나의 분야인 디지털 영상 분류기법의 연구현황을 확인하며, 디지털 영상 분류기법의 필요성에 대하여 설명하도록 하겠다.

• Fashion & Textile Research Journal
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• v.18 no.1
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• pp.27-37
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• 2016

This study examined consumer perceptions towards outdoor wear differentiation and product attributes for outdoor wear differentiation to develop an outdoor wear differentiation strategy. It also investigated how consumer's evaluated product attributes according to consumer's demographic characteristics. Data were acquired from a survey of 454 adult respondents aged over 20 that was analyzed with descriptives, frequency, t-test, one-way ANOVA, factor analysis, and reliability. The results were as follows. First, it showed that consumers did not have a positive or a negative perception toward outdoor wear differentiation, and they thought outdoor wear should be differentiated. Those married and older tended to think that outdoor wear should be differentiated more than that for those single and younger. Consumer evaluations were significantly different on the necessity of outdoor wear differentiation according to age and total income. Second, consumers assessed that color, pattern and textiles had similar characteristics among outdoor wear brands; in addition, brand recognition and brand image had very different characteristics. Third, product attributes for outdoor wear differentiation were service and store, product quality, brand and popularity, and product designs with mean values of product quality, product design, service and store, and brand and popularity, respectively. Fourth, consumers were significantly different in the importance assessment of product attributes for differentiation according to gender, marital status and age.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1017-1021
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• 2013

The traffic signal has been widely used in the transport system with a fixed time interval currently. This kind of setting time was determined based on experience for vehicles to generate a waiting time while allowing pedestrians crossing the street. However, this strict setting causes inefficient problems in terms of economic and safety crossing. In this research, we propose a monitoring algorithm to detect, track and check pedestrian crossing the crosswalk by the patterns of behavior. This monitoring system ensures the safety for pedestrian and keeps the traffic flow in efficient. In this algorithm, pedestrians are detected by using HOG feature which is robust to illumination changes in outdoor environment. According to a complex computation, the parallel process with the GPU as well as CPU is adopted for real-time processing. Therefore, pedestrians are tracked by the relationship of hue channel in image sequence according to the predefined pedestrian zone. Finally, the system checks the pedestrians' crossing on the crosswalk by its HOG based behavior patterns. In experiments, the parallel processing by both GPU and CPU was performed so that the result reaches 16 FPS (Frame Per Second). The accuracy of detection and tracking was 93.7% and 91.2%, respectively.