• The Journal of Korean Institute of Next Generation Computing
• /
• v.15 no.4
• /
• pp.40-50
• /
• 2019

In recent years, it is important to visualize an accurate human body model for the low-end system in the medical imaging field where augmented reality technology and virtual reality technology are used. Decreasing the geometry of a model causes a difference from the original shape and considers the difference as an error. So, the error should be minimized while reducing geometry. In this study, the organ areas of a human body in the tomographic images such as CT or MRI is segmented and 3D geometric model is generated, thereby implementing the reconstruction method of multiple resolution level-of-detail model. In the experiment, a virtual reality platform was constructed to verify the shape of the reconstructed model, targeting the spine area. The 3D human body model and patient information can be verified using the virtual reality platform.

• The Journal of Information Systems
• /
• v.31 no.4
• /
• pp.145-160
• /
• 2022

Purpose The purpose of this study is to develop a virtual try-on deep learning model that can efficiently learn front and back clothes images. It is expected that the application of virtual try-on clothing service in the fashion and textile industry field will be vitalization. Design/methodology/approach The data used in this study used 232,355 clothes and product images. The image data input to the model is divided into 5 categories: original clothing image and wearer image, clothing segmentation, wearer's body Densepose heatmap, wearer's clothing-agnosting. We advanced the HR-VITON model in the way of Mixed-Precison, Gradient Accumulation, and sharing model weights. Findings As a result of this study, we demonstrated that the weight-shared MP-GA HR-VITON model can efficiently learn front and back fashion images. As a result, this proposed model quantitatively improves the quality of the generated image compared to the existing technique, and natural fitting is possible in both front and back images. SSIM was 0.8385 and 0.9204 in CP-VTON and the proposed model, LPIPS 0.2133 and 0.0642, FID 74.5421 and 11.8463, and KID 0.064 and 0.006. Using the deep learning model of this study, it is possible to naturally fit one color clothes, but when there are complex pictures and logos as shown in <Figure 6>, an unnatural pattern occurred in the generated image. If it is advanced based on the transformer, this problem may also be improved.

• Journal of Korean Society of Rural Planning
• /
• v.18 no.4
• /
• pp.129-140
• /
• 2012

Land suitability assessment assesses development, farming, and conservation suitability, considering land's soil, location, and possibility for use. It also implement segmentation of management regions into production, conservation, and plan management area. It is evaluated as a very significant system in establishing a land use system of sustainable development and development after planning in the aspect that it can establish proper land use plan. This study developed a recommendation model for development in agent-based model that interacts with surrounding lands. It also tried to summarize the area characteristic analysis and the results of land suitability evaluation, targeting three ri's in Yesan-Gun, and analyze the model's applicability by selection results. In order to recommend area for development that considers the use of the surrounding lands, it calculated development possibility indices that considered the ratings of all the lands in the target areas for each parcel and simulated the model. As a result, selected three areas in target region were suitable areas for development in land suitability assessment. In detail, ratings of the recommended parcels were 3, 4, and 5 ratings. As a result of examining the land status, it showed that all the three areas were plan management areas, thus easy for development. It is judged that the model for recommending area for development suggested in this study can be used as important basic data for setting the direction for development when establishing a regional planning.

• The Journal of the Korea Contents Association
• /
• v.9 no.5
• /
• pp.1-12
• /
• 2009

The correct detection of skin color is an important preliminary process in fields of face detection and human motion analysis. It is generally performed by three steps: transforming the pixel color to a non-RGB color space, dropping the illuminance component of skin color, and classifying the pixels by the skin color distribution model. Skin detection depends on by various factors such as color space, presence of the illumination, skin modeling method. In this paper we propose a 3d skin color model that can segment pixels with several ethnic skin color from images with various illumination condition and complicated backgrounds. This proposed skin color model are formed with each components(Y, Cb, Cr) which transform pixel color to YCbCr color space. In order to segment the skin color of several ethnic groups together, we first create the skin color model of each ethnic group, and then merge the skin color model using its skin color probability. Further, proposed model makes several steps of skin color areas that can help to classify proper skin color areas using small training data.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.4
• /
• pp.75-86
• /
• 2004

This paper proposes a method to speed up the document image binarization using a water flow model. The proposed method extracts the region of interest (ROI) around characters from a document image and restricts pouring water onto a 3-dimensional terrain surface of an image only within the ROI. The amount of water to be filed into a local valley is determined automatically depending on its depth and slope. The proposed method accumulates weighted water not only on the locally lowest position but also on its neighbors. Therefore, a valley is filed enough with only one try of pouring water onto the terrain surface of the ROI. Finally, the depth of each pond is adaptively thresholded for robust character segmentation, because the depth of a pond formed at a valley varies widely according to the gray-level difference between characters and backgrounds. In our experiments on real document images, the Proposed method has attained good binarization performance as well as remarkably reduced processing time compared with that of the existing method based on a water flow model.

• Journal of Korean Society on Water Environment
• /
• v.32 no.6
• /
• pp.589-599
• /
• 2016

Implemented since 2004, TPLC (Total Pollution Load Control) is the most powerful water-quality protection program. Recently, uncertainty of prediction using steady state model increased due to changing water environments, and necessity of a dynamic state model, especially the watershed model, gained importance. For application of watershed model on TPLC, it needs to be feasible to adjust the relationship (mass-balance) between discharged loads estimated by technical guidance, and arrived loads based on observed data at the watershed outlet. However, at HSPF, simulation is performed as a semi-distributed model (lumped model) in a sub-basin. Therefore, if the estimated discharged loads from individual pollution source is directly entered as the point source data into the RCHRES module (without delivery ratio), the pollutant load is not reduced properly until it reaches the outlet of the sub-basin. The hypothetic RCHRES generated using the HSPF BMP Reach Toolkit was applied to solve this problem (although this is not the original application of Reach Toolkit). It was observed that the impact of discharged load according to spatial distribution of pollution sources in a sub-basin, could be expressed by multi-segmentation of the hypothetical RCHRES. Thus, the discharged pollutant load could be adjusted easily by modification of the infiltration rate or characteristics of flow control devices.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1106-1110
• /
• 2004

A human sign language is generally composed of both static and dynamic gestures. Each gesture is represented by a hand shape, its position, and hand movement (for a dynamic gesture). One of the problems found in automated sign language translation is on segmenting a hand movement that is part of a transitional movement from one hand gesture to another. This transitional gesture conveys no meaning, but serves as a connecting period between two consecutive gestures. Based on the observation that many dynamic gestures as appeared in Thai sign language dictionary are of quasi-periodic nature, a method was developed to differentiate between a (meaningful) dynamic gesture and a transitional movement. However, there are some meaningful dynamic gestures that are of non-periodic nature. Those gestures cannot be distinguished from a transitional movement by using the signal quasi-periodicity. This paper proposes a hybrid method using a combination of the periodicity-based gesture segmentation method with a HMM-based gesture classifier. The HMM classifier is used here to detect dynamic signs of non-periodic nature. Combined with the periodic-based gesture segmentation method, this hybrid scheme can be used to identify segments of a transitional movement. In addition, due to the use of quasi-periodic nature of many dynamic sign gestures, dimensionality of the HMM part of the proposed method is significantly reduced, resulting in computational saving as compared with a standard HMM-based method. Through experiment with real measurement, the proposed method's recognition performance is reported.

• Journal of the Korean Home Economics Association
• /
• v.37 no.4
• /
• pp.41-58
• /
• 1999

The purposes of this study were 1) to segment the market based on purchase frequency of products such as apparel, food, home electronics, life commodity in department store and low-price retailing, 2) to identify differences among segments in belief and attitude toward each store, purchase frequency of apparel items in each store and demographic variables. The data were collected via a self-administered questionnaire from 274 married women living in Seoul, Korea and analyzed by factor analysis, cluster analysis, one-way ANOVA and x$^2$-test. The results of this study were as follows: First, using cluster analysis on purchase frequency of products in each store, four groups were identified and labeled as department store patronage/ non-purchasers of apparel in low-price retailing(25.2%), purchasers of apparel in department store and low-price retailing(16.8%), low-price retailing patronage(30.3%) and non-purchasers of products in department store and low-price retailing(27.0%). Second, a series of one-way ANOV As revealed significant differences among four segments on beliefs of low-price retailing(four store attributes: price and variety of apparel product, facilities for convenient shopping, promotion, brand-reputation and fashionability of apparel product) and department store(three store attributes: price and variety of apparel product, facilities for convenient shopping and promotion) and attitude toward low-price retailing and department store. Attitude toward each store was yielded using Fishbein's multiattributes model. There were also significant differences among groups in purchase frequency of seven apparel items in low-price retailing and six apparel items in department store, and six demographic and personal variables(age, educational status, type of husband's occupation, monthly income and housing). Finally, the papers discussed manageral implications for each segments as well as theoretical implications.

• The KIPS Transactions:PartB
• /
• v.9B no.6
• /
• pp.765-774
• /
• 2002

The purpose of this paper is a design and implementation for korean character and pen-gesture recognition system in multimedia terminal, PDA and etc, which demand both a fast process and a high recognition rate. To recognize writing-types which are written by various users, the korean character recognition system uses a database which is based on the characteristic information of korean and the stroke information Which composes a phoneme, etc. In addition. it has a fast speed by the phoneme segmentation which uses the successive process or the backtracking process. The pen-gesture recognition system is performed by a matching process between the classification features extracted from an input pen-gesture and the classification features of 15 pen-gestures types defined in the gesture model. The classification feature is using the insensitive stroke information. i.e., the positional relation between two strokes. the crossing number, the direction transition, the direction vector, the number of direction code. and the distance ratio between starting and ending point in each stroke. In the experiment, we acquired a high recognition rate and a fart speed.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.5
• /
• pp.55-64
• /
• 2018

In this paper, we propose fully convolutional neural networks based automatic detection of a pothole that directly causes driver's safety accidents and the vehicle damage. First, the training DB is collected through the camera installed in the vehicle while driving on the road, and the model is trained in the form of a semantic segmentation using the fully convolutional neural networks. In order to generate robust performance in a dark environment, we augmented the training DB according to brightness, and finally generated a total of 30,000 training images. In addition, a total of 450 evaluation DB was created to verify the performance of the proposed automatic pothole detection, and a total of four experts evaluated each image. As a result, the proposed pothole detection showed robust performance for missing.