• Radiation Oncology Journal
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• 제37권2호
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• pp.134-142
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• 2019

Purpose: The aim is to study the dependence of deformable based auto-segmentation of head and neck organs-at-risks (OAR) on anatomy matching for a single atlas based system and generate an acceptable set of contours. Methods: A sample of ten patients in neutral neck position and three atlas sets consisting of ten patients each in different head and neck positions were utilized to generate three scenarios representing poor, average and perfect anatomy matching respectively and auto-segmentation was carried out for each scenario. Brainstem, larynx, mandible, cervical oesophagus, oral cavity, pharyngeal muscles, parotids, spinal cord, and trachea were the structures selected for the study. Automatic and oncologist reference contours were compared using the dice similarity index (DSI), Hausdroff distance and variation in the centre of mass (COM). Results: The mean DSI scores for brainstem was good irrespective of the anatomy matching scenarios. The scores for mandible, oral cavity, larynx, parotids, spinal cord, and trachea were unacceptable with poor matching but improved with enhanced bony matching whereas cervical oesophagus and pharyngeal muscles had less than acceptable scores for even perfect matching scenario. HD value and variation in COM decreased with better matching for all the structures. Conclusion: Improved anatomy matching resulted in better segmentation. At least a similar setup can help generate an acceptable set of automatic contours in systems employing single atlas method. Automatic contours from average matching scenario were acceptable for most structures. Importance should be given to head and neck position during atlas generation for a single atlas based system.

• 한국프랜차이즈경영연구
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• 제7권1호
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• pp.21-29
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• 2016

Purpose - The primary purpose of this study is to employ effective marketing methods using market segmentation of coffee shops by determining how motivations to visit coffee shops have different impacts on demographic profile of visitors and characteristics of coffee shop visits, so as to draw out a better understanding of customers of coffee market. Research design, data, and methodology - Data were collected using surveys of self-administered questionnaires toward coffee shop users in Daejeon, Korea. A number of samples used in data analysis were 253 excluding unusable responses. The data were analyzed through frequency, reliability, and factor analysis using SPSS 20.0. Factor analysis was conducted through the principal component analysis and varimax rotation method to derive factors of one or more eigen values. In addition, the cluster analysis, multivariate ANOVA, and cross-tab analysis were used for the market segmentation based on the types of motivation for coffee shop visits. The process of the cluster analysis is as follows. Four clusters were derived through hierarchical clustering, and k-means cluster analysis was then carried out using mean value of the four clusters as the initial seed value. Result - The factor analysis delineated four dimensions of motivation to visit coffee shops: ostentation motivation, hedonic motivation, esthetic motivation, utility motivation. The cluster analysis yielded four clusters: utility and esthetic seekers, hedonic seekers, utility seekers, ostentation seekers. In order to further specify the profile of four clusters, each cluster was cross tabulated with socio-demographics and characteristics of coffee shop visits. Four clusters are significantly different from each other by four types of motivations for coffee shop visits. Conclusions - This study has empirically examined the difference in demographic profile of visitors and characteristics of coffee shop visits by motivation to visit coffee shops. There are significant differences according to age, education background, marital status, occupation and monthly income. In addition, coffee shops use pattern characterization in frequency of visits to coffee shops, relationships with companion, purpose of visit, information sources, brand type, average expense per visit, important elements of selection attribute were significantly different depending on motivations for coffee shop visits.

• 대한원격탐사학회지
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• 제36권5_3호
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• pp.1095-1107
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• 2020

홍수 발생 시 위성영상을 활용하여 침수된 지역을 추출하는 것은 홍수 발생 기간 내의 위성영상 취득과 영상에 나타난 침수구역의 정확한 분류 등에서 많은 어려움이 존재한다. 딥러닝은 전통적인 영상분류기법들에 비해 보다 정확도가 높은 위성영상분류기법으로 주목받고 있지만, 광학영상에 비해 홍수 발생 시 위성영상의 취득이 용이한 SAR 영상의 분류 잠재력은 아직 명확히 규명되지 않았다. 본 연구는 대표적인 의미론적 영상 분할을 위한 딥러닝 모델인 SegNet과 U-Net을 활용하여 동남아시아의 라오스, 태국, 필리핀의 대표적인 홍수 발생지역인 코랏 유역(Khorat basin), 메콩강 유역(Mekong river basin), 카가얀강 유역(Cagayan river basin)에 대해 Sentinel-1 A/B 위성영상으로부터 침수지역 추출을 실시하였다. 분석결과 침수지역 탐지에서 SegNet의 Global Accuracy, Mean IoU, Mean BF Score는 각각 0.9847, 0.6016, 0.6467로 나타났으며, U-Net의 Global Accuracy, Mean IoU, Mean BF Score는 각각 0.9937, 0.7022, 0.7125로 나타났다. 국지적 분류결과 확인을 위한 육안검증에서 U-Net이 SegNet에 비해 보다 높은 분류 정확도를 보여주었지만, 모델의 훈련에 필요한 시간은 67분 17초와 187분 19초가 각각 소요되어 SegNet이 U-Net에 비해 약 3배 정도 빠른 처리속도를 보여주었다. 본 연구의 결과는 향후 딥러닝 기법을 활용한 SAR 영상기반의 홍수탐지 모델과 실무적으로 활용이 가능한 자동화된 딥러닝 기반의 수계탐지 기법의 제시를 위한 중요한 참고자료로 활용될 수 있을 것으로 판단된다.

• 한국산학기술학회논문지
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• 제18권11호
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• pp.705-710
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• 2017

본 논문에서는 해충 영상에서 객체 분할과 SVM 분류기를 이용한 복숭아순나방의 개체 수 추정 방법을 제안한다. 과수원에 설치된 페로몬 트랩에 수집된 복숭아순나방 영상에 대해 객체 분할과 개체 분류를 수행하였다. 객체 분할은 전처리, 문턱치 처리, 형태학적 필터링, 객체 레이블링 과정으로 구성된다. 해충 영상에서 복숭아순나방의 개체 분류는 SVM 분류기의 학습과 개체 분류, 개체 수 추정 단계로 구성된다. 객체 분할은 SVM 분류기에 입력하기 전에 객체들을 분할함으로써 개체 분류 단계에서 처리 과정을 단순하게 해 준다. 분할된 객체들에 대해 중심점과 주축을 중심으로 영상 블록을 추출하여 SVM 분류기에 입력한다. 실험에서 10개의 해충 영상에 대해 복숭아순나방의 개체 수 추정 결과 97%의 평균 추정 정확도를 보임으로써 과수원에서 복숭아순나방의 개체 모니터링 방법으로서 효과적임을 보였다. 또한 제안한 방법의 처리 시간은 평균 2.4초, 슬라이딩 윈도우 방식은 5.7초로 본 논문의 방법이 약 2.4배 정도 처리 시간이 빠름을 보였다.

• 한국산학기술학회논문지
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• 제13권5호
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• pp.1996-2001
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• 2012

본 시각 검사시스템은 전자 부품과 같이 잘 형성된 표면 재료에 널리 사용되고 있다. 반사 능력이 나쁜 재료의 경우, 시각 검사시스템이 도입될 때 많은 문제점이 발생한다. 혼합 생산 라인에서 진위의 모델을 알 수 없을 때 저 반사비와 많은 노이즈에도 잘 작동할, 강인한 시각 검사시스템을 개발하였다. 유형을 인식하기 위하여 k-means를 이용한 작업물 인식 기법이 제안되었다. 인식 유형에 기반하여 active contour라는 노이즈에 강인한 분할 기법이 영상에서 특징을 분할하는데 응용되었다. 오차 변화를 조정하는데 Kalman 필터가 사용되었다. 자동시각 검사시스템의 실험은 프로젝터를 이용한 수작업 측정의 정확도 수준을 보여준다.

• 한국의학물리학회지:의학물리
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• 제24권2호
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• pp.99-107
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• 2013

간 전이 암은 이전에는 수술을 통한 외과적 절제가 주요 치료기법이었지만 방사선 치료 기법의 발전으로 인해 점차 방사선치료의 시행이 늘어나고 있다. 18F-FDG PET 영상은 간 전이 암 진단 시 더욱 우세한 민감도와 특이도를 보이며, 치료계획용 CT 영상과 더불어 종양조직의 위치를 정의하는 중요한 영상장비로 자리매김하고 있다. 본 연구에서는 간 전이 암의 18F-FDG PET 영상에 나타난 종양영역을 영상분할기법 적용하였으며 PET영상의 여러 인자들이 영상분할기법들에 미치는 영향을 알아보았다. 2009년부터 2012년까지 방사선 치료를 받은 간전이 환자들 중 18F-FDG PET/CT 촬영을 시행한 13명의 환자들의 치료계획용 CT와 PET/CT 영상을 얻었다. 그 뒤 PET 영상의 관심영역을 설정하기 위하여 3가지 영상 분할 기법인 상대적문턱기법, 기울기기법, 영역성장기법을 적용하였다. 이 결과들을 바탕으로 GTV와 각 영상 기법으로 구현된 종양 영역과 부피 비교를 시행하였으며 영상 분할 기법에 영향을 미치는 영상인자들과의 관계를 회귀 분석하였다. GTV (Gross Tumor Volume)의 평균 부피는 $60.9{\pm}65.9$ cc이며, 40% 상대적문턱값 기법은 $22.43{\pm}35.3$ cc, 50% 상대적문턱값 기법은 $10.11{\pm}17.9$ cc, 영역성장기법은 $32.89{\pm}36.8$ cc, 기울기기법은 $30.34{\pm}35.8$ cc로 나타났다. 기존의 GTV와 가장 유사한 영역을 나타낸 영상 분할 기법은 영역성장기법 이었다. 이 영역성장기법에 영향을 미치는 영상인자를 정량적으로 분석하기 위해 표준화 계수 ${\beta}$값을 이용하였으며, GTV의 크기, $TumorSUV_{MAX/MIN}$, $SUV_{max}$, TBR 순으로 나타났다. 이와 같은 PET 영상인자를 반영한 영상 분할 기법을 이용해서 종양 영역을 정의한다면 보다 정확하고 일관성 있는 종양그리기를 수행할 수 있으며 궁극적으로 종양에 최적화된 방사선량을 투여할 수 있을 것이다.

• International Journal of Contents
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• 제11권2호
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• pp.15-19
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• 2015

In this paper, we propose an efficient document layout analysis algorithm that includes table detection. Typical methods of document layout analysis use the height and gap between words or columns. To correspond to the various styles and sizes of documents, we propose an algorithm that uses the mean value of the distance transform representing thickness and compare with components in the local area. With this algorithm, we combine a table detection algorithm using the same feature as that of the text classifier. Table candidates, separators, and big components are isolated from the image using Connected Component Analysis (CCA) and distance transform. The key idea of text classification is that the characteristics of the text parallel components that have a similar thickness and height. In order to estimate local similarity, we detect a text region using an adaptive searching window size. An improved adaptive run-length smoothing algorithm (ARLSA) was proposed to create the proper boundary of a text zone and non-text zone. Results from experiments on the ICDAR2009 page segmentation competition test set and our dataset demonstrate the superiority of our dataset through f-measure comparison with other algorithms.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권3호
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• pp.166-171
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• 2009

In recent years, video detection systems have been implemented in various infrastructures such as airport, public transportation, power generation system, water dam and so on. Recognizing moving objects in video sequence is an important problem in computer vision, with applications in several fields, such as video surveillance and target tracking. Segmentation and tracking of multiple vehicles in crowded situations is made difficult by inter-object occlusion. In the system described in this paper, the mean shift algorithm is firstly used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate or not. And then some characters in the license plate is recognized by using the fuzzy ARTMAP neural network, which is a relatively new architecture of the neural network family and has the capability to learn incrementally unlike the conventional BP network. We finally design a license plate recognition system using the mean shift algorithm and fuzzy ARTMAP neural network and show its performance via some computer simulations.

• 패션비즈니스
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• 제18권4호
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• pp.97-113
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• 2014

This study investigates consumer's traits of market segmentation based on the apparel benefits. The subjects were 302 college students living in Yunnam, China. The mean, ANOVA, factor analysis, Duncan test, and K-means cluster analysis were used for statisticals analysis. The results of this study are as follows. The college students were classified, into six subdivisions, according to the apparel benefits by cluster analysis: indifference group, utility pursuit group, hedonic/brand pursuit group, individuality pursuit group, social recognition/fashion pursuit group, and pursuit benefits-minded group. In the factors of happiness-pursuing and life-centered of materialism, significant differences were found according to the groups of apparel benefits, and all factors of symbolic consumption and brand loyalty were found to have significant differences according to the groups of apparel benefits. The evaluation criteria of clothing were significantly different, depending on apparel benefits subdivision in criteria of aesthetic, socio-psychological, and utility. The use of information was shown to have significant differences, according to the groups of apparel benefits. The study results are highly expected to be utilized as useful sources in marketing plans for the midwest of China.

• 대한원격탐사학회지
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• 제27권3호
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• pp.379-388
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• 2011

At present, GEOBIA (Geographic Object-based Image Analysis), heir of OBIA (Object-based Image Analysis), is regarded as an important methodology by object-oriented paradigm for remote sensing, dealing with geo-objects related to image segmentation and classification in the different view point of pixel-based processing. This also helps to directly link to GIS applications. Thus, GEOBIA software is on the booming. The main theme of this study is to look into the applicability of geo-spatial processing open source to GEOBIA. However, there is no few fully featured open source for GEOBIA which needs complicated schemes and algorithms, till It was carried out to implement a preliminary system for GEOBIA running an integrated and user-oriented environment. This work was performed by using various open sources such as OTB or PostgreSQL/PostGIS. Some points are different from the widely-used proprietary GEOBIA software. In this system, geo-objects are not file-based ones, but tightly linked with GIS layers in spatial database management system. The mean shift algorithm with parameters associated with spatial similarities or homogeneities is used for image segmentation. For classification process in this work, tree-based model of hierarchical network composing parent and child nodes is implemented by attribute join in the semi-automatic mode, unlike traditional image-based classification. Of course, this integrated GEOBIA system is on the progressing stage, and further works are necessary. It is expected that this approach helps to develop and to extend new applications such as urban mapping or change detection linked to GIS data sets using GEOBIA.