• 한국멀티미디어학회논문지
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• 제13권7호
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• pp.1000-1015
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• 2010

본 논문에서는 손동작 인식을 위한 새로운 방법을 제안한다. 손 추출을 위한 방법으로는 피부색과 boundary energy 정보를 이용하고 moment method로 손바닥의 중심을 구하게 된다. 손동작 인식은 두 단계로 나눌 수 있다. 첫 번째 단계는 손 형상 인식으로 병렬 신경망을 이용하였다. 손 형상의 패턴을 추출하기 위해서 fitting ellipses method를 이용하였다. fitting ellipses method는 추출된 손 영역을 12개의 타원형으로 분류하고 타원 외곽선의 흰 픽셀 비율을 계산한다. 패턴은 12개의 입력 노드로 신경망에 입력되고 4개의 출력 노드로 출력되는데 각 출력 노드는 0~1사이의 값을 갖게 된다. 손 형상은 4개의 출력 노드의 구성으로 나타낼 수 있다. 두 번째 단계는 손동작 추적과 인식이다. 손동작 추적과 인식을 위해서는 손동작의 위치 정보를 예측 할 수 있는 Kalman Filter를 이용하였다. 실험은 Windows XP상에서 수행되었고 제안한 알고리즘의 효율성을 평가하였다. 손 형상을 인식하기 위해서 300개의 이미지를 인식기에 훈련시키고 200개의 이미지를 실험에 사용하였다. 194개 이상의 이미지가 정확하게 인식 되었다. 그리고 손동작 추적 인식을 실험하기 위해서 1200번의 손동작(각 동작은 400번)을 사용하였고 그 중 1002번의 손동작이 정확하게 인식 되었다.이러한 결과는 제안된 방법이 손 영역을 추출하고 손 동작을 인식하는데 유용함을 확인 할 수 있었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1491-1494
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• 2002

The paper introduces a novel system of two hands real-time tracking based on the unrestricted hand skin segmentation by multi color systems. After corer-based segmentation and pre-processing operation, a label set of regions is created to locate the two hands automatically. By the normalization, template matching is used to find out the left or right hand. An improved fast self-adaptive tracking algorithm is applied and Canny filter is used for hand detection.

• Computers and Concrete
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• 제27권5호
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• pp.447-455
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• 2021

Research on damage detection methods in structures began a few decades ago with the introduction of methods based on structural vibration frequencies, which, of course, continues to this day. The value of important structures, on the one hand, and the countless maintenance costs on the other hand, have led researchers to always try to identify more accurate methods to diagnose damage to structures in the early stages. Among these, one of the most important and widely used methods in damage detection is the use of time-frequency representations. By using time-frequency representations, it is possible to process signals simultaneously in the time and frequency domains. In this research, the Short-Time Fourier transform, a known time-frequency function, has been used to process signals and identify the system. Besides, a new damage index has been introduced to identify damages in concrete piers of bridges. The proposed method has relatively simple calculations. To evaluate the method, the finite element model of an existing concrete bridge was created using as-built details. Based on the results, the method identifies the damages with high accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.82-86
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• 2004

A fuzzy rule-based hand-motion estimation algorithm is proposed for a 6 dimensional spatial tracker in which low cost accelerometers and gyros are employed. To be specific, beginning and stopping of hand motions needs to be accurately detected to initiate and terminate integration process to get position and pose of the hand from accelerometer and gyro signals, since errors due to noise and/or hand-shaking motions accumulated by integration processes. Fuzzy rules of yes or no of hand-motion-detection are here proposed for rules of accelerometer signals, and sum of derivatives of accelerometer and gyro signals. Several experimental results and shown to validate our proposed algorithms.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.553-556
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• 2005

We present a vision-based hand gesture recognition system for object manipulation in virtual space. Most conventional hand gesture recognition systems utilize a simpler method for hand detection such as background subtractions with assumed static observation conditions and those methods are not robust against camera motions, illumination changes, and so on. Therefore, we propose a statistical method to recognize and detect hand regions in images using geometrical structures. Also, Our hand tracking system employs multiple cameras to reduce occlusion problems and non-synchronous multiple observations enhance system scalability. Experimental results show the effectiveness of our method.

• 대한임베디드공학회논문지
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• 제14권3호
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• pp.133-142
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• 2019

The existing auxiliary communicating aids for the hearing-impaired have an inconvenience of using additional expensive sensing devices. This paper presents a hand image detection based algorithm to interpret the sign language of the hearing-impaired. The proposed sign language recognition system exploits the hand image only captured by the camera without using any additional gloves with extra sensors. Based on the hand image processing, the system can perfectly classify several numeric sign language representations. This work proposes a simple lightweight classification algorithm to identify the hand image of the hearing-impaired to communicate with others even further in an environment of complex background. Experimental results show that the proposed system can interpret the numeric sign language quite well with an accuracy of 95.6% on average.

• 제어로봇시스템학회논문지
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• 제18권4호
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• pp.328-336
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• 2012

In this paper, we propose a robust fingertip extraction and extended Continuously Adaptive Mean Shift (CAMSHIFT) based robust hand gesture recognition for natural human-like HRI (Human-Robot Interaction). Firstly, for efficient and rapid hand detection, the hand candidate regions are segmented by the combination with robust $YC_bC_r$ skin color model and haar-like features based adaboost. Using the extracted hand candidate regions, we estimate the palm region and fingertip position from distance transformation based voting and geometrical feature of hands. From the hand orientation and palm center position, we find the optimal fingertip position and its orientation. Then using extended CAMSHIFT, we reliably track the 2D hand gesture trajectory with extracted fingertip. Finally, we applied the conditional density propagation (CONDENSATION) to recognize the pre-defined temporal motion trajectories. Experimental results show that the proposed algorithm not only rapidly extracts the hand region with accurately extracted fingertip and its angle but also robustly tracks the hand under different illumination, size and rotation conditions. Using these results, we successfully recognize the multiple hand gestures.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.664-667
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• 2009

There is strong demand to create wearable PC systems that can support the user outdoors. When we are outdoors, our movement makes it impossible to use traditional input devices such as keyboards and mice. We propose a hand gesture interface based on image processing to operate wearable PCs. The semi-transparent PC screen is displayed on the head mount display (HMD), and the user makes hand gestures to select icons on the screen. The user's hand is extracted from the images captured by a color camera mounted above the HMD. Since skin color can vary widely due to outdoor lighting effects, a key problem is accurately discrimination the hand from the background. The proposed method does not assume any fixed skin color space. First, the image is divided into blocks and blocks with similar average color are linked. Contiguous regions are then subjected to hand recognition. Blocks on the edges of the hand region are subdivided for more accurate finger discrimination. A change in hand shape is recognized as hand movement. Our current input interface associates a hand grasp with a mouse click. Tests on a prototype system confirm that the proposed method recognizes hand gestures accurately at high speed. We intend to develop a wider range of recognizable gestures.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제1권3호
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• pp.177-186
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• 2012

본 논문에서는 실시간 손동작 분석을 위한 깊이정보 기반 손 영역 검출 및 추적 방법을 제안한다. 이를 위해 손 영역 검출단계에서는 깊이정보만을 이용하여 손 영역의 특징인 형태모델을 생성하고, 검출 시 움직임 정보와 영역 확장(Region Growing)을 통해 객체를 추출한다. 추출된 객체는 사전에 생성된 형태모델과 크기정보를 분석하여 최종 손 영역으로 판정한다. 판정된 손 객체는 추적단계에서 중심점 전이 과정을 통해 이전 중심점과의 최근접점을 획득하고, 최근접점으로부터 영역 확장과 깊이기반 적응적 평균 이동 기법(DAM-Shift)을 통해 새로운 중심점을 검출하여 추적한다. 마지막으로 성능 검증을 위해 다양한 손 모양과 속도 및 위치에 대한 다양한 환경에서 실험하고, 검출속도와 추적된 궤적의 정량적, 정성적 분석을 통해 제안하는 방법의 효율성을 입증한다.