• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.05a
• /
• pp.343-346
• /
• 2004

The purpose of our research is to identify the critical variables and to develop a new methodology for market segmentation of online game market. Our research tested the model with Korean online game users because Korean online game industry is the frontier of global online game industries. Conclusively, the critical variables are the suitability of feedback, the reality of design, the precision of information and the involvement of virtual community. The analysis of segmentation shows that the primary target audiences are positively influenced by the reality of design and the involvement of virtual community. To attract the primary target audiences, online game companies should develop strategies depending on the effectiveness of the variables and the demographic and behavioral characteristics of target audiences.

• Journal of the Institute of Convergence Signal Processing
• /
• v.17 no.2
• /
• pp.67-70
• /
• 2016

This paper discusses a graphic user interface(GUI) for the concealed target segmentation. The human subject hiding a metal gun is captured by the passive millimeter wave(MMW) imaging system. The imaging system operates on the regime of 8 mm wavelength. The MMW image is analyzed by the multi-level segmentation to segment and identify a concealed weapon under clothing. The histogram of the passive MMW image is modeled with the Gaussian mixture distribution. LBG vector quantization(VQ) and expectation and maximization(EM) algorithms are sequentially applied to segment the body and the object area. In the experiment, the GUI is implemented by the MFC(Microsoft Foundation Class) and the OpenCV(Computer Vision) libraries and tested in real-time showing the efficiency of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.6
• /
• pp.2302-2316
• /
• 2015

We propose an efficient method of extracting targets within a region of interest in non-homogeneous infrared images by using a principal component analysis (PCA) plane and adaptive Gaussian kernel. Existing approaches for extracting targets have been limited to using only the intensity values of the pixels in a target region. However, it is difficult to extract the target regions effectively because the intensity values of the target region are mixed with the background intensity values. To overcome this problem, we propose a novel PCA based approach consisting of three steps. In the first step, we apply a PCA technique minimizing the total least-square errors of an IR image. In the second step, we generate a binary image that consists of pixels with higher values than the plane, and then calculate the second derivative of the sum of the square errors (SDSSE). In the final step, an iteration is performed until the convergence criteria is met, including the SDSSE, angle and labeling value. Therefore, a Gaussian kernel is weighted in addition to the PCA plane with the non-removed data from the previous step. Experimental results show that the proposed method achieves better segmentation performance than the existing method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.9
• /
• pp.2458-2482
• /
• 2023

Medical image segmentation techniques based on convolution neural networks indulge in feature extraction triggering redundancy of parameters and unsatisfactory target localization, which outcomes in less accurate segmentation results to assist doctors in diagnosis. In this paper, we propose a multi-level semantic-rich encoding-decoding network, which consists of a Pooling-Conv-Former (PCFormer) module and a Cbam-Dilated-Transformer (CDT) module. In the PCFormer module, it is used to tackle the issue of parameter explosion in the conservative transformer and to compensate for the feature loss in the down-sampling process. In the CDT module, the Cbam attention module is adopted to highlight the feature regions by blending the intersection of attention mechanisms implicitly, and the Dilated convolution-Concat (DCC) module is designed as a parallel concatenation of multiple atrous convolution blocks to display the expanded perceptual field explicitly. In addition, MultiHead Attention-DwConv-Transformer (MDTransformer) module is utilized to evidently distinguish the target region from the background region. Extensive experiments on medical image segmentation from Glas, SIIM-ACR, ISIC and LGG demonstrated that our proposed network outperforms existing advanced methods in terms of both objective evaluation and subjective visual performance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2020.11a
• /
• pp.33-36
• /
• 2020

Virtual try on is getting interested from researchers these days because its application in online shopping. But single pose virtual try on is not enough, customer may want to see themselves in different pose. Multiple pose virtual try on is getting input as customer image, an in-shop cloth and a target pose, it will try to generate realistic customer wearing the in-shop cloth with the target pose. We first generate the target segmentation layout using conditional generative network (cGAN), and then the in-shop cloth are warped to fit the customer body in target pose. Finally, all the result will be combine using a Resnet-like network. We experiment and show that our method outperforms stage of the art.

• Progress in Medical Physics
• /
• v.13 no.4
• /
• pp.181-186
• /
• 2002

The IMRT planning depends on the algorithm of each planning system and MLC performance of each Linac system. Yonsei Cancer Center introduced an IMRT System at the beginning of February, 2002. The system consists of CORVUS (Nomos, U.S.A.) treatment planning system, LANTIS, PRIMEVIEW and PRIMART (Siemens, U.S.A) linac system. The optimization of CORVUS planning system with PRIMART is an important task to make a desirable quality treatment plan. Our Step ＆ Shoot IMRT system uses Finite Size Pencil Beams (FSPB) dose model, simulated annealing optimization algorithm and IMFAST segmentation algorithm. We constructed treatment plans for four different patient cases with two basic beamlet sizes, 1.0$\times$1.0 $\textrm{cm}^2$ and 0.5$\times$1.0 $\textrm{cm}^2$, and four intensity steps, 5%, 10%, 20%, 33%. Each case's plan was evaluated with the dose volume histograms of target volumes and delivery efficiencies. The patient case of small target volume is sensitive at the change of intensity map's segmentation and it highlighted an effective treatment plan at marrow intensity step and small basic projection beamlet.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.6
• /
• pp.195-202
• /
• 2005

An adaptive segmentation algorithm is used to efficiently target decisions in local non-stationary images. Until now, several adaptive approaches have been proposed as a method of segmentation. However, they can't be directly used for radar target detection because a radar signal has different characteristics from general images. Generally, a histogram of radar signal shows that targets have a relatively small number of frequency functions compared to the background and distribution of background, which have several shapes as the environment changes. In this paper, we propose an adaptive segmentation algorithm using a histogram chord which is a right-down line from maximum pick of frequency function. The proposed method provides thresholds which are optimum for several radar environments because the used chord for threshold search is not significantly effected by interference conditions. Simulation results show that the proposed method is superior to the traditional algorithms, global threshold method and distribution median method, with respect to detection performance.

• Journal of The Korean Digital Architecture Interior Association
• /
• v.8 no.1
• /
• pp.39-47
• /
• 2008

This study aims to provide the guidelines for planning and operating convention centers. A case study is utilized with the units of analysis, including Seoul COEX, Busan BEXCO, Daegu EXCO, Jeju ICCJEJU, Ilsan KINTEX, Gwangju KDJ Center, and Changwon CECO. The findings related to the operation of convention centers in Korea are summarized as follows: the interation of similar conventions; an increase in size; globalization; and specialization. With a view of marketing, this study concludes as follows: (1) initial categorization of target markets is needed due mainly to the founding purposes and the site characteristics of the convention centers in question while utilizing market segmentation strategies; (2) a specific target market should be selected and focused on it; and (3) the development strategy of an image and servicescape is demanded to devised in plan.

• Journal of Information Processing Systems
• /
• v.14 no.6
• /
• pp.1405-1419
• /
• 2018

An image fusion method is proposed on the basis of depth model segmentation to overcome the shortcomings of noise interference and artifacts caused by infrared and visible image fusion. Firstly, the deep Boltzmann machine is used to perform the priori learning of infrared and visible target and background contour, and the depth segmentation model of the contour is constructed. The Split Bregman iterative algorithm is employed to gain the optimal energy segmentation of infrared and visible image contours. Then, the nonsubsampled contourlet transform (NSCT) transform is taken to decompose the source image, and the corresponding rules are used to integrate the coefficients in the light of the segmented background contour. Finally, the NSCT inverse transform is used to reconstruct the fused image. The simulation results of MATLAB indicates that the proposed algorithm can obtain the fusion result of both target and background contours effectively, with a high contrast and noise suppression in subjective evaluation as well as great merits in objective quantitative indicators.

• Journal of Advanced Navigation Technology
• /
• v.10 no.1
• /
• pp.48-56
• /
• 2006

This paper proposes an efficient preprocessing technique for a binary centroid tracker in correlated image sequences. It is known that the following factors determine the performance of the binary centroid target tracker: (1) an efficient real-time preprocessing technique, (2) an exact target segmentation from cluttered background images and (3) an intelligent tracking window sizing, and etc. The proposed centroid tracker consists of an adaptive segmentation method based on novel distance features and an efficient real-time preprocessing technique in order to enhance the distinction between the objects of interest and their local background. Various tracking experiments using synthetic images as well as real Forward-Looking InfraRed (FLIR) images are performed to show the usefulness of the proposed methods.