• Radiation Oncology Journal
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• v.37 no.2
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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.

• The Korean Journal of Franchise Management
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• v.7 no.1
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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.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1095-1107
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• 2020

Flood monitoring using satellite data has been constrained by obtaining satellite images for flood peak and accurately extracting flooded areas from satellite data. Deep learning is a promising method for satellite image classification, yet the potential of deep learning-based flooded area extraction using SAR data remained uncertain, which has advantages in obtaining data, comparing to optical satellite data. This research explores the performance of SegNet and U-Net on image segmentation by extracting flooded areas in the Khorat basin, Mekong river basin, and Cagayan river basin in Thailand, Laos, and the Philippines from Sentinel-1 A/B satellite data. Results show that Global Accuracy, Mean IoU, and Mean BF Score of SegNet are 0.9847, 0.6016, and 0.6467 respectively, whereas those of U-Net are 0.9937, 0.7022, 0.7125. Visual interpretation shows that the classification accuracy of U-Net is higher than SegNet, but overall processing time of SegNet is around three times faster than that of U-Net. It is anticipated that the results of this research could be used when developing deep learning-based flood monitoring models and presenting fully automated flooded area extraction models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.705-710
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• 2017

This paper proposes an estimation method of populations of Grapholita molestas using object segmentation and an SVM classifier in the moth images. Object segmentation and moth classification were performed on images of Grapholita molestas moth acquired on a pheromone trap equipped in an orchard. Object segmentation consisted of pre-processing, thresholding, morphological filtering, and object labeling process. The classification of Grapholita molestas in the moth images consisted of the training and classification of an SVM classifier and estimation of the moth populations. The object segmentation simplifies the moth classification process by segmenting the individual objects before passing an input image to the SVM classifier. The image blocks were extracted around the center point and principle axis of the segmented objects, and fed into the SVM classifier. In the experiments, the proposed method performed an estimation of the moth populations for 10 moth images and achieved an average estimation precision rate of 97%. Therefore, it showed an effective monitoring method of populations of Grapholita molestas in the orchard. In addition, the mean processing time of the proposed method and sliding window technique were 2.4 seconds and 5.7 seconds, respectively. Therefore, the proposed method has a 2.4 times faster processing time than the latter technique.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.1996-2001
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• 2012

Visual inspection systems are widely proposed for the well formed surface materials like electronics parts. But the materials with ill reflection ability have many troubles when visual inspection system is introduced. We have developed a robust visual inspection system that can work well in spite of low reflection ratio and with much noise when truth model is not known in the mixed production line. A workpiece identification technique using k-means has been proposed to identify the type. Based on the identified type, a robust-to-noise segmentation method, called active contour, has been applied to segment the features from the image. Finally, Kalman filter has been applied to adapt the error variation. Experiment shows that performance is about to match the accuracy of manual measurement using projectors.

• Progress in Medical Physics
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• v.24 no.2
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• pp.99-107
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• 2013

The surgical resection was occurred mainly in liver metastasis before the development of radiation therapy techniques. Recently, Radiation therapy is increased gradually due to the development of radiation dose delivery techniques. 18F-FDG PET image showed better sensitivity and specificity in liver metastasis detection. This image modality is important in the radiation treatment with planning CT for tumor delineation. In this study, we applied automatic image segmentation methods on PET image of liver metastasis and examined the impact of image factors on these methods. We selected the patients who were received the radiation therapy and 18F-FDG PET/CT in Korea Cancer Center Hospital from 2009 to 2012. Then, three kinds of image segmentation methods had been applied; The relative threshold method, the Gradient method and the region growing method. Based on these results, we performed statistical analysis in two directions. 1. comparison of GTV and image segmentation results. 2. performance of regression analysis for relation between image factor affecting image segmentation techniques. The mean volume of GTV was $60.9{\pm}65.9$ cc and the $GTV_{40%}$ was $22.43{\pm}35.27$ cc, and the $GTV_{50%}$ was $10.11{\pm}17.92$ cc, the $GTV_{RG}$ was $32.89{\pm}36.8$4 cc, the $GTV_{GD}$ was $30.34{\pm}35.77$ cc, respectively. The most similar segmentation method with the GTV result was the region growing method. For the quantitative analysis of the image factors which influenced on the region growing method, we used the standardized coefficient ${\beta}$, factors affecting the region growing method show GTV, $TumorSUV_{MAX/MIN}$, $SUV_{max}$, TBR in order. The result of the region growing (automatic segmentation) method showed the most similar result with the CT based GTV and the region growing method was affected by image factors. If we define the tumor volume by the auto image segmentation method which reflect the PET image parameters, more accurate and consistent tumor contouring can be done. And we can irradiate the optimized radiation dose to the cancer, ultimately.

• International Journal of Contents
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• v.11 no.2
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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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• v.9 no.3
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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.

• Journal of Fashion Business
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• v.18 no.4
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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.

• Korean Journal of Remote Sensing
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• v.27 no.3
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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.