• 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.

• 한국정보기술학회 영문논문지
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• 제10권1호
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• pp.71-83
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• 2020

In this paper, we propose a system that can detect the shape of a hand at high speed using an FPGA. The hand-shape detection system is designed using Verilog HDL, a hardware language that can process in parallel instead of sequentially running C++ because real-time processing is important. There are several methods for hand gesture recognition, but the image processing method is used. Since the human eye is sensitive to brightness, the YCbCr color model was selected among various color expression methods to obtain a result that is less affected by lighting. For the CbCr elements, only the components corresponding to the skin color are filtered out from the input image by utilizing the restriction conditions. In order to increase the speed of object recognition, a median filter that removes noise present in the input image is used, and this filter is designed to allow comparison of values and extraction of intermediate values at the same time to reduce the amount of computation. For parallel processing, it is designed to locate the centerline of the hand during scanning and sorting the stored data. The line with the highest count is selected as the center line of the hand, and the size of the hand is determined based on the count, and the hand and arm parts are separated. The designed hardware circuit satisfied the target operating frequency and the number of gates.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 1998년도 가을 학술발표논문집 Vol.25 No.2 (2)
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• pp.384-386
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• 1998

The use of hand gesture provides an attractive alternative to cumbersome interface devices for human-computer interaction(HCI). Many methods hand gesture recognition using visual analysis have been proposed such as syntactical analysis, neural network(NN), Hidden Markov Model(HMM) and so on. In our research, a HMMs is proposed for alphabetical hand gesture recognition. In the preprocessing stage, the proposed approach consists of three different procedures for hand localization, hand tracking and gesture spotting. The hand location procedure detects the candidated regions on the basis of skin-color and motion in an image by using a color histogram matching and time-varying edge difference techniques. The hand tracking algorithm finds the centroid of a moving hand region, connect those centroids, and thus, produces a trajectory. The spotting a feature database, the proposed approach use the mesh feature code for codebook of HMM. In our experiments, 1300 alphabetical and 1300 untrained gestures are used for training and testing, respectively. Those experimental results demonstrate that the proposed approach yields a higher and satisfying recognition rate for the images with different sizes, shapes and skew angles.

• 대한임베디드공학회논문지
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• 제16권1호
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• pp.17-27
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• 2021

In this paper, we develop a galaxy image classification method based on hand-crafted features and machine learning techniques. Additionally, we provide an empirical analysis to reveal which combination of the techniques is effective for galaxy image classification. To achieve this, we developed a framework which consists of four modules such as preprocessing, feature extraction, feature post-processing, and classification. Finally, we found that the best technique for galaxy image classification is a method to use a median filter, ORB vector features and a voting classifier based on RBF SVM, random forest and logistic regression. The final method is efficient so we believe that it is applicable to embedded environments.

• 한국콘텐츠학회논문지
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• 제18권4호
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• pp.398-405
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• 2018

HCI 분야에서 대표적인 손 제스처 인식은 IT기기의 개발과 더불어 사용자와 기기 간의 상호작용 및 정보교환을 위한 방법으로 주목받고 있다. 영상 처리를 통한 손 제스처 인식에서 손바닥 영역 검출은 처리속도 및 인식률 향상에 기여하는 핵심 처리 과정이다. 본 논문에서는 손바닥 영역 검출(palm area detection)을 위해 손과 손목을 영상 분할(image segmentation) 하는 새로운 방법을 제안한다. 손의 해부학적 특성으로 가장 넓은 폭이 발생하는 엄지와 소지의 장골 간격을 손 영상의 수평 투사 히스토그램으로 계산 후 이 간격을 지름으로 하는 원을 그려 손바닥 영역을 검출한다. 이 방법의 우수성을 검증하기 위하여 다단 형판정합(multiple stage template matching)을 사용해 10가지 손 제스처에 대해 기존 방법 4가지와 인식 성능을 비교 평가한다. 손 제스처 인식에 관련한 연구가 다양하나 손바닥 영역 검출에 특화된 성능 비교 문헌이 저조함을 강조한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.187-189
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• 2004

This paper presents the system widely used for extraction of human body recognition system in the field of bio-metric identification. The Human body recognition system is used in many fields. This biological is appled to the human recognition in banking and the access control with security. The important algorithm of the identification software usese hand lines and hand shape geometry. We used the simple algorithm and recognizing the person by their hand image from the input camera. The geometrical characteristics in hand shape such as length of finger to whole hand length thickness of finger to length, etc are used.

• 정보처리학회논문지B
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• 제18B권3호
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• pp.109-116
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• 2011

본 논문에서는 기계적인 조작 장치 없이 손동작만으로 컴퓨터를 조작할 수 있는 차세대 인터페이스인 에어 인터페이스를 구현하였다. 에어 인터페이스 시스템 구현을 위해 먼저 적외선의 전반사 원리를 이용하였으며, 이후 획득된 적외선 영상에서 손 영역을 분할한다. 매 프레임에서 분할된 손 영역은 이벤트 처리를 위한 손동작 인식부의 입력으로 사용되고, 최종적으로 개별 제어 이벤트에 맵핑된 손동작 인식을 통하여 일반적인 제어를 수행하게 된다. 본 연구에서는 손영역 검출과 추적, 손동작 인식과정을 위해 구현되어진 영상처리 및 인식 기법들이 소개되며, 개발된 에어 인터페이스 시스템은 길거리 광고, 프레젠테이션, 키오스크 등의 그 활용성이 매우 클 것으로 기대된다.

• IEIE Transactions on Smart Processing and Computing
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• 제4권5호
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• pp.340-348
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• 2015

The "stamp rally" is an event that participants go the round with predetermined points for the purpose of collecting stamps. They bring the stamp card to these points. They, however, sometimes leave or lose the card. In this case, they may not reach the final destination of the stamp rally. The purpose of this research is the construction of the stamp rally system which distinguishes each participant with his or her hand instead of the stamp card. We have realized our method distinguishing a hand posture by the image processing. We have also evaluated it by 30 examinees. Furthermore, we have designed the data communication between the server and the checkpoint to implement our whole system. We have also designed and implemented the process for the registering participant, the passing checkpoint and the administration.

• 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.

• 한국기계가공학회지
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• 제20권12호
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• pp.1-7
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• 2021

The deep learning image processing technique was used to prevent accidents in lathe work caused by worker negligence. During lathe operation, when the chuck is rotated, it is very dangerous if the operator's hand is near the chuck. However, if the chuck is stopped during operation, it is not dangerous for the operator's hand to be in close proximity to the chuck for workpiece measurement, chip removal or tool change. We used YOLO (You Only Look Once), a deep learning image processing program for object detection and classification. Lathe work images such as hand, chuck rotation and chuck stop are used for learning, object detection and classification. As a result of the experiment, object detection and class classification were performed with a success probability of over 80% at a confidence score 0.5. Thus, we conclude that the artificial intelligence deep learning image processing technique can be effective in preventing incidents resulting from worker negligence in future manufacturing systems.