• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2839-2852
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• 2013

Context-awareness is an essential part of ubiquitous computing, and over the past decade video based activity recognition (VAR) has emerged as an important component to identify user's context for automatic service delivery in context-aware applications. The accuracy of VAR significantly depends on the performance of the employed human body segmentation algorithm. Previous human body segmentation algorithms often engage modeling of the human body that normally requires bulky amount of training data and cannot competently handle changes over time. Recently, active contours have emerged as a successful segmentation technique in still images. In this paper, an active contour model with the integration of Chan Vese (CV) energy and Bhattacharya distance functions are adapted for automatic human body segmentation using depth cameras for VAR. The proposed technique not only outperforms existing segmentation methods in normal scenarios but it is also more robust to noise. Moreover, it is unsupervised, i.e., no prior human body model is needed. The performance of the proposed segmentation technique is compared against conventional CV Active Contour (AC) model using a depth-camera and obtained much better performance over it.

• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.234-240
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• 2016

We propose a novel framework for markerless camera-based games. By using a visual camera, our method may yield robust human body segmentation with high performance comparable to the segmentation using depth cameras. The edges of human bodies are detected by subtracting gradient backgrounds, and human body regions are segmented by the operations based on mathematical morphology. Collisions between detected regions and virtual objects are determined by finding the colliding time using intra-frame positions of virtual objects. Experimental results show that the proposed method may produce robust segmentation of human bodies, thereby and the collision responses are more accurate than previous methods. Therefore, the proposed framework can be widely used in camera-based games requiring high performance.

• Advances in robotics research
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• v.2 no.2
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• pp.129-140
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• 2018

With the increasing demand for the development of human pose estimation, such as human-computer interaction and human activity recognition, there have been numerous approaches to detect the 2D poses of people in images more efficiently. Despite many years of human pose estimation research, the estimation of human poses with images remains difficult to produce satisfactory results. In this study, we propose a robust 2D human body pose estimation method using an RGB camera sensor. Our pose estimation method is efficient and cost-effective since the use of RGB camera sensor is economically beneficial compared to more commonly used high-priced sensors. For the estimation of upper-body joint positions, semantic segmentation with a fully convolutional network was exploited. From acquired RGB images, joint heatmaps accurately estimate the coordinates of the location of each joint. The network architecture was designed to learn and detect the locations of joints via the sequential prediction processing method. Our proposed method was tested and validated for efficient estimation of the human upper-body pose. The obtained results reveal the potential of a simple RGB camera sensor for human pose estimation applications.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.1
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• pp.68-79
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• 2009

It is inevitable to have cutting line to get the 2D pattern from 3D body surface. In this paper the efficiency of curvature plot as a cutting line in the process of flattening 3D surface was investigated. As reference, basic clothing construction line was adopted to divide the 3D surface into small blocks to make the flattening process easy. Female dummy as well as human body were scanned and surface of the upper body was segmented using curvature plot and basic constructing line. 2D tight-fit pattern was developed using three software, the RapidForm 2004, 2C-AN and Yuka CAD. Gap between clothes and body, and the clothing pressure on the body was observed to determine the fit of the clothes. As results, clothes constructed with blocks divided by curvature plot displayed a similar level of tight fit as compared with those by basic construction line. It was found that curvature plot is effective method as a segmentation of the 3D surface even for the actual body which does not have any previous reference line. It is expected that application of curvature plot will be expanded in 3D apparel technology.

• Progress in Medical Physics
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• v.15 no.2
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• pp.112-119
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• 2004

We tried to build human models based on medical images of live Korean, instead of using standard data of human body structures. Characteristics of mechanical structures of human bodies were obtained from medical images such as CT and MR images. For each constitutional part of mechanical structures CT images were analyzed in terms of gray levels and MR images were analyzed in terms of pulse sequence. Characteristic features of various mechanical structures were extracted from the analyses. Based on the characteristics of each structuring element we peformed image segmentation on CT and MR images. We delineated bones, muscles, ligaments and tendons from CT and MR images using image segmentation or manual drawing. For the image segmentation we compared the edge detection method, region growing method and intensity threshold method and applied an optimal compound of these methods for the best segmentation results. Segmented mechanical structures of the head/neck part were three dimensionally reconstructed.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.9_10 s.146
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• pp.1359-1368
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• 2005

When we develop the tight-fit 2D pattern from the 3D scan data, segmentation of the 3D scan data into several parts is necessary to make a curved surface into a flat plane. In this study, Garland's method of triangle simplification was adopted to reduce the number of data point without distorting the original shape. The Runge-Kutta method was applied to make triangular patch from the 3D surface in a 2D plane. We also explored the detailed arrangement method of small 2D patches to make a tight-fit pattern for a male body. As results, minimum triangle numbers in the simplification process and efficient arrangement methods of many pieces were suggested for the optimal 2D pattern development. Among four arrangement methods, a block method is faster and easier when dealing with the triangle patches of male's upper body. Anchoring neighboring vertices of blocks to make 2D pattern was observed to be a reasonable arrangement method to get even distribution of stress in a 2D plane.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.6
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• pp.1070-1086
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• 2020

This study analyzed the size of women in their 60s, 70s and 80s in the 2014 Human Body Dimension Survey data among Size Korea's 7th Human Dimension Survey Data in 2015. This study examined the characteristics of changes in female body shape according to aging and the age range. The height item gradually decreased from the 60s and then rapidly decreased to the 80s. In the case of the circumference item, a sharp change occurred in the section from 69 to 70 years old. The reduction in hip height, waist height, and navel level waist height was not significant compared to the decrease in stature. Both width, thickness, and circumference gradually decreased with age. It can be seen that the back bends forward and the legs become thinner than the body due to the aging phenomenon with increasing age. Even for older women, the characteristics of aging differ in their 60s, 70s and 80s, so age segmentation of silver clothing should be considered.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.593-603
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• 2009

In this paper, we propose a new integrated computer vision system designed to track multiple human beings and extract their silhouette with an active stereo camera. The proposed system consists of three modules: detection, tracking and silhouette extraction. Detection was performed by camera ego-motion compensation and disparity segmentation. For tracking, we present an efficient mean shift based tracking method in which the tracking objects are characterized as disparity weighted color histograms. The silhouette was obtained by two-step segmentation. A trimap is estimated in advance and then this was effectively incorporated into the graph cut framework for fine segmentation. The proposed system was evaluated with respect to ground truth data and it was shown to detect and track multiple people very well and also produce high quality silhouettes. The proposed system can assist in gesture and gait recognition in field of Human-Robot Interaction (HRI).

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

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