• Journal of Korea Multimedia Society
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• v.16 no.9
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• pp.1031-1043
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• 2013

Extracting hand region from images is the first part in the process to recognize hand posture and gesture interaction. Therefore, a good segmenting method is important because it determines the overall performance of hand recognition systems. Conventional hand segmentation researches were prone to changing illumination conditions or limited to the ability to detect multiple people. In this paper, we propose a robust technique based on the fusion of skin-color data and depth information for hand segmentation process. The proposed algorithm uses skin-color data to localize accurate seed location for region-growing from a complicated background. Based on the seed location, our algorithm adjusts each detected blob to fill up the hole region. A region-growing algorithm is applied to the adjusted blob boundary at the detected depth image to obtain a robust hand region against illumination effects. Also, the resulting hand region is used to train our skin-model adaptively which further reduces the effects of changing illumination. We conducted experiments to compare our results with conventional techniques which validates the robustness of the proposed algorithm and in addition we show our method works well even in a counter light condition.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1003-1006
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• 1999

Hand segmentation and tracking is essential to the development of a hand gesture recognition system. This research features segementation and tracking of hand regions based the hue component of color. We propose a method that employs HSI color model, and segments and tracks hand regions using the hue component of color alone. In order to track the segmented hand regions, we only apply Kalman filter to a region of interest represented by a rectangle region. Initial experimental results show that the system accurately segments and tracks hand regions although it only uses the hue compoent of color. The system yields near real time throghput of 8 frames per second on a Pentium II 233MHz PC.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.309-318
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• 2001

In this paper, we describe methods that analyze a human gesture. A human interface(HI) system for analyzing gesture extracts the head and hand regions after taking image sequence of and operators continuous behavior using CCD cameras. As gestures are accomplished with operators head and hands motion, we extract the head and hand regions to analyze gestures and calculate geometrical information of extracted skin regions. The analysis of head motion is possible by obtaining the face direction. We assume that head is ellipsoid with 3D coordinates to locate the face features likes eyes, nose and mouth on its surface. If was know the center of feature points, the angle of the center in the ellipsoid is the direction of the face. The hand region obtained from preprocessing is able to include hands as well as arms. For extracting only the hand region from preprocessing, we should find the wrist line to divide the hand and arm regions. After distinguishing the hand region by the wrist line, we model the hand region as an ellipse for the analysis of hand data. Also, the finger part is represented as a long and narrow shape. We extract hand information such as size, position, and shape.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1603-1613
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• 2017

This paper proposes a method for effectively segmenting the hand region using a widely popular RGB color-based webcam. This performs the empirical preprocessing method four times to remove the noise. First, we use Gaussian smoothing to remove the overall image noise. Next, the RGB image is converted into the HSV and the YCbCr color model, and global fixed binarization is performed based on the statistical value for each color model, and the noise is removed by the bitwise-OR operation. Then, RDP and flood fill algorithms are used to perform contour approximation and inner area fill operations to remove noise. Finally, ROI (hand region) is selected by eliminating noise through morphological operation and determining a threshold value proportional to the image size. This study focuses on the noise reduction and can be used as a base technology of gesture recognition application.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.504-516
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• 2011

In this paper, we will present a method to detect human hand and recognize hand gesture. For detecting the hand region, we use the feature of human skin color and hand feature (with boundary complexity) to detect the hand region from the input image; and use algorithm of optical flow to track the hand movement. Hand gesture recognition is composed of two parts: 1. Posture recognition and 2. Motion recognition, for describing the hand posture feature, we employ the Fourier descriptor method because it's rotation invariant. And we employ PCA method to extract the feature among gesture frames sequences. The HMM method will finally be used to recognize these feature to make a final decision of a hand gesture. Through the experiment, we can see that our proposed method can achieve 99% recognition rate at environment with simple background and no face region together, and reduce to 89.5% at the environment with complex background and with face region. These results can illustrate that the proposed algorithm can be applied as a production.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.121-127
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• 2013

A vision based hand-gesture interface method for substituting a pointing device is proposed in this paper, which is used the ratio and size variation of Gesture Region. Proposed method uses the skin hue&saturation of the hand region from the HSI color model to extract the hand region effectively. This method can remove the non-hand region, and reduces the noise effect by the light source. Also, as the computation quantity is reduced by detecting not the static hand-shape recognition, but the ratio and size variation of hand-moving from the clipped hand region in real time, more response speed is guaranteed. In order to evaluate the performance of the our proposed method, after applying to the computerized self visual acuity testing system as a pointing device. As a result, the proposed method showed the average 86% gesture recognition ratio and 87% coordinate moving recognition ratio.

• Journal of Korea Multimedia Society
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• v.15 no.12
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• pp.1449-1455
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• 2012

This paper has developed a system for early diagnosis of senile dementia and mild cognitive impairment (MCI) by developing software to measure the volume of hippocampus. This software consists of two parts; segmentation and analysis. The segmentation part uses ROI and region growing to segment hippocampus region. On the other hand, the analysis part creates a volume rendering of hippocampus. This software is expected contribute in these research fields for dementia diagnosis and its medication planning.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.398-405
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• 2018

In the HCI, hand gesture recognition is attracting attention as a method for interaction and information exchange between users and devices along with the development of IT devices. In hand gesture recognition through image processing, palm region detection is a key process contributing to improvement of processing speed and recognition rate. In this paper, we propose a new method for image segmentation between the hand and wrist for palm area detection. The anatomical characteristics of the hand are used to calculate the distance between the iliac bones of the thumb and little finger, which have the widest width, by the horizontal projection histogram of the hand image, and then the palm area is detected by drawing a circle having the width as the diameter. In order to verify the superiority of this method, multiple stage template matching is used to compare and evaluate recognition performance against the four conventional methods for 10 hand gestures. Note that the literatures to offer palm area detection performance evaluation are few although there are many studies on hand gesture recognition.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.705-716
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• 2002

This paper proposes a method which visualizes MRI head data in 3 dimensions with direct volume rendering. Though surface rendering is usually used for MRI data visualization, it has some limits of displaying little speckles because it loses the information of the speckles in the surfaces while acquiring the information. Direct volume rendering has ability of displaying little speckles, but it doesn't treat MRI data because of the data features of MRI. In this paper, we try to visualize MRI head data in 3 dimensions as follows. First, we separate the brain region from the head region of MRI head data, next increase the pixel level of the brain region, then combine the brain region with the increased pixel level and the head region without brain region, last visualizes the combined MRI head data with direct volume rendering. We segment the brain region from head region based on histogram threshold, morphology operations and snakes algorithm. The proposed segmentation method shows 91~95% similarity with a hand segmentation. The method rather clearly visualizes the organs of the head in 3 dimensions.

• Journal of International Society for Simulation Surgery
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• v.1 no.1
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• pp.27-31
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• 2014

In this paper, an overview of segmentation and 3D visualization methods are presented. Commonly, the two kinds of methods are used to visualize organs and vessels into 3D from medical images such as CT(A) and MRI - Direct Volume Rendering (DVR) and Iso-surface Rendering (IR). DVR can be applied directly to a volume. It directly penetrates through the volume while it determines which voxels are visualizedbased on a transfer function. On the other hand, IR requires a series of processes such as segmentation, polygonization and visualization. To extract a region of interest (ROI) from the medical volume image via the segmentation, some regions of an object and a background are required, which are typically obtained from the user. To visualize the extracted regions, the boundary points of the regions should be polygonized. In other words, the boundary surface composed of polygons such as a triangle and a rectangle should be required to visualize the regions into 3D because illumination effects, which makes the object shaded and seen in 3D, cannot be applied directly to the points.