• The Transactions of the Korea Information Processing Society
• /
• v.6 no.8
• /
• pp.2262-2270
• /
• 1999

In this paper, we describe gesture recognition algorithm using computer vision sensor and HMM. The automatic hand region extraction has been proposed for initializing the tracking of the tele-operation gestures. For this, distance informations(disparity map) as results of stereo matching of initial left and right images are employed to isolate the hand region from a scene. PDOE(positive difference of edges) feature images adapted here have been found to be robust against noise and background brightness. The KNU/KAERI(K/K) gesture instruction set is defined for tele-operation in atomic electric power stations. The composite recognition model constructed by concatenating three gesture instruction models including pre-orders, basic orders, and post-orders has been proposed and identified by discrete HMM. Our experimental results showed that consecutive orders composed of more than two ones are correctly recognized at the rate of above 97%.

• Journal of Biomedical Engineering Research
• /
• v.26 no.6
• /
• pp.357-363
• /
• 2005

Active contour models have been extensively used to segment, match, and track objects of interest in computer vision and image processing applications, particularly to locate object boundaries. With conventional methods an object boundary can be extracted by controlling the internal energy and external energy based on energy minimization. However, this still leaves a number of problems, such as initialization and poor convergence in concave regions. In particular, a contour is unable to enter a concave region based on the stretching and bending characteristic of the internal energy. Therefore, this study proposes a method that controls the internal energy by moving the local perpendicular bisector point of each control point on the contour, and determines the object boundary by minimizing the energy relative to the external energy. Convergence at a concave region can then be effectively implemented as regards the feature of interest using the internal energy, plus several objects can be detected using a multi-detection method based on the initial contour. The proposed method is compared with other conventional methods through objective validation and subjective consideration. As a result, it is anticipated that the proposed method can be efficiently applied to the detection of the pulmonary parenchyma region in medical images.

• Science of Emotion and Sensibility
• /
• v.5 no.4
• /
• pp.1-10
• /
• 2002

A web based virtual try-on provides customers a more enjoyable shopping experience that visualize clothes on personal mannequin. The researchers compared virtual fitting models which were developed in 2000 at Korea and in 2000 and 2002 at U.S. The results of this study as follows: The information about user's body size was required to input for selection of a virtual fitting model. 7 to 19 different body size, shape, and face features including weight and height were needed for visualizing virtual fitting model. The body type of the U.S virtual fitting model(My virtual model) was selected by front view silhouette for women, and by shoulder width and midriff silhouette for men. The more detailed information was required for developing Korean virtual fitting model. The additional body size information required in the site were leg and arm lengths, waist length, and thigh and ankle circumferences. The body proportion of Korean cyber personal mannequin was longer and narrower than the U.S cyber personal mannequin. It was recommended that standardized body length, width, and depth proportions calculated from national anthropometric data must be applied for developing Korean virtual fitting model. With application of more detailed information on face feature and advanced graphic image technology the 'My virtual model in 2002 resembled the human body shape of various race.

• The Korean Journal of Applied Statistics
• /
• v.16 no.1
• /
• pp.87-100
• /
• 2003

Most of statistical data are generated by a set of dynamic, stochastic, and simultaneous relations. An important question is how to specify statistical models so that they are consistent with the dynamic feature of those data. A general hypothesis is that the lagged effect of a change in an explanatory variable is not felt all at once at a single point in time, but The impact is distributed over a number of future points in time. In other words, current control variables are determined by a function that can be reduced to a distributed lag function of past observations. It is possible to explain the relationship between variables in different points of time and to estimate the long-run impacts of a change in a variable on another if time lag series of explanatory variables are incorporated in the model specification. In this study, distributed lag structure is applied to the domestic stock market model to capture the dynamic response of the market by exogenous shocks. The Domestic market is found more responsive to the changes in foreign market factors both in the short and the long run.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.41 no.11
• /
• pp.1035-1045
• /
• 2017

Classification of age and gender has been carried out through different approaches such as facial-based and audio-based classifications. One of the limitations of facial-based methods is the reduced recognition rate over large distances, while another is the prerequisite of the faces to be located in front of the camera. Similarly, in audio-based methods, the recognition rate is reduced in a noisy environment. In contrast, gait-based methods are only required that a target person is in the camera. In previous works, the view point of a camera is only available as a side view and gait data sets consist of a standard gait, which is different from an ordinary gait in a real environment. We propose a feature extraction method using skeleton models from an RGB-D sensor by considering characteristics of age and gender using ordinary gait. Experimental results show that the proposed method could efficiently classify age and gender within a target group of individuals in real-life environments.

• Journal of Fashion Business
• /
• v.16 no.1
• /
• pp.103-120
• /
• 2012

The study aimed to evaluate the appearance of the men's work clothes jumpers developed to suggest the prototype work clothes jumper patterns by using the 3-D clothing simulation technology. The 3-D simulated clothing images considered the upper body features of men in the age range between 20 and 59 in South Korea. A questionnaire survey conducted previously suggested a basic jumper style with shirt collar and snap opening cuffs for the heavy industry workers; and discomforting parts of the work clothes jumper of the subject workers have been referred to for the experimental jumper appearance test. Besides, defining the measurements of men's upper bodies enabled to generate the men's 3-D virtual models representing each age group's average body feature. The significant body measurement factors for men's 3-D body modeling and jumper pattern-making were stature for the height factor; chest, waist and hip circumferences for the circumference factor; waist back, hip and arm lengths and interscye front/back for the length factor; and back neck breadth for the breadth factor and armscye and scye depths for the depth factor. The men's body measurements of 30's were implemented to three experimental jumper pattern-making methods, i.e. the 1st method using the relations based on stature and chest circumference; the 2nd method using the direct body measurements; and the 3rd method adopting the maximum ease amount of given body measurements whether relations or direct measurements except the direct measurement of scye depth. A comparison among the three experimental jumpers' simulated images highlighted that the appropriate ease amount of the jumper gained higher scores in terms of the jumpers' front, side, back and sleeve parts and the total silhouettes. Therefore the 3rd experimental jumper was finally selected for the heavy industry workers.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.2
• /
• pp.769-786
• /
• 2016

Object recognition and object location have always drawn much interest. Also, recently various computational models have been designed. One of the big issues in this domain is the lack of an appropriate model for extracting important part of the picture and estimating the object place in the same environments that caused low accuracy. To solve this problem, a new Salient Based Bag of Visual Word (SBBoVW) model for object recognition and object location estimation is presented. Contributions lied in the present study are two-fold. One is to introduce a new approach, which is a Salient Based Bag of Visual Word model (SBBoVW) to recognize difficult objects that have had low accuracy in previous methods. This method integrates SIFT features of the original and salient parts of pictures and fuses them together to generate better codebooks using bag of visual word method. The second contribution is to introduce a new algorithm for finding object place based on the salient map automatically. The performance evaluation on several data sets proves that the new approach outperforms other state-of-the-arts.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.4
• /
• pp.1824-1845
• /
• 2016

The GMM is a conventional approach which has been recently applied in many face recognition studies. However, the question about how to deal with illumination changes while ensuring high performance is still a challenge, especially with real-world databases. In this paper, we propose a Visual Observation Confidence (VOC) measure for robust face recognition for illumination changes. Our VOC value is a combined confidence value of three measurements: Flatness Measure (FM), Centrality Measure (CM), and Illumination Normality Measure (IM). While FM measures the discrimination ability of one face, IM represents the degree of illumination impact on that face. In addition, we introduce CM as a centrality measure to help FM to reduce some of the errors from unnecessary areas such as the hair, neck or background. The VOC then accompanies the feature vectors in the EM process to estimate the optimal models by modified-GMM training. In the experiments, we introduce a real-world database, called KoFace, besides applying some public databases such as the Yale and the ORL database. The KoFace database is composed of 106 face subjects under diverse illumination effects including shadows and highlights. The results show that our proposed approach gives a higher Face Recognition Rate (FRR) than the GMM baseline for indoor and outdoor datasets in the real-world KoFace database (94% and 85%, respectively) and in ORL, Yale databases (97% and 100% respectively).

• Journal of the Korean Geosynthetics Society
• /
• v.12 no.1
• /
• pp.11-19
• /
• 2013

Coarse-grained soils are typical engineering materials commonly used in many civil engineering applications such as structural fills, subgrade and drainage fills for dam, railway and bridge. Various researches have been performed with related to constitutive laws for numerical analysis of such structures. This paper presents a parametric study for a constitutive model for coarse grained materials. The model is a kind of the bounding surface models based on critical state theory. A distinct feature of the model is to capture the response of coarse-grained materials with different void ratios and confining pressures using a single set of model parameters. The model behavior is defined with a set of elastic parameters, critical state parameters, and model-specific parameters. The parametric study was performed for the model-specific parameters. The result of parametric study shows that the model is capable to capture stress-dilatancy behavior and kinematic-hardening under non-associative plastic flow.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.15 no.6
• /
• pp.579-589
• /
• 2004

The radar signals are generally very sensitive to relative orientations between radar and target. Thus, the performance of a target recognition system significantly deteriorates as the region of aspect angles becomes broader. To address this difficulty, in this paper, we propose a method based on the multi-aspect information in order to improve the classification capability ever for a wide angular region. First, range profiles are used to extract feature vectors based on the central moments and principal component analysis(PCA). Then, a classifier with the use of multi-aspect information is applied to them, yielding an additional improvement of target recognition capability. There are two different strategies among the classifiers that can fuse the information from multi-aspect radar signals: independent methodology and dependent methodology. In this study, the performances of the two strategies are compared within the frame work of target recognition. The radar cross section(RCS) data of six aircraft models measured at compact range of Pohang University of Science and Technology are used to demonstrate and compare the performances of the two strategies.