• 대한임베디드공학회논문지
• /
• 제14권6호
• /
• pp.295-311
• /
• 2019

The smart product market is growing year by year and is being used in many areas. There are various ways of interacting with smart products and users by inputting voice recognition, touch and finger movements. It is most important to detect an accurate hand region as a whole step to recognize hand movement. In this paper, we propose a method to detect accurate hand region in real time in various environments. A conventional method of detecting a hand region includes a method using depth information of a multi-sensor camera, a method of detecting a hand through machine learning, and a method of detecting a hand region using a color model. Among these methods, a method using a multi-sensor camera or a method using a machine learning requires a large amount of calculation and a high-performance PC is essential. Many computations are not suitable for embedded systems, and high-end PCs increase or decrease the price of smart products. The algorithm proposed in this paper detects the hand region using the color model, corrects the problems of the existing hand detection algorithm, and detects the accurate hand region based on various experimental environments.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제4권4호
• /
• pp.618-632
• /
• 2010

This paper presents a novel approach to control the augmented reality (AR) objects robustly in a marker-less AR system by fingertip tracking and hand pattern recognition. It is known that one of the promising ways to develop a marker-less AR system is using human's body such as hand or face for replacing traditional fiducial markers. This paper introduces a real-time method to manipulate the overlaid virtual objects dynamically in a marker-less AR system using both hands with a single camera. The left bare hand is considered as a virtual marker in the marker-less AR system and the right hand is used as a hand mouse. To build the marker-less system, we utilize a skin-color model for hand shape detection and curvature-based fingertip detection from an input video image. Using the detected fingertips the camera pose are estimated to overlay virtual objects on the hand coordinate system. In order to manipulate the virtual objects rendered on the marker-less AR system dynamically, a vision-based hand control interface, which exploits the fingertip tracking for the movement of the objects and pattern matching for the hand command initiation, is developed. From the experiments, we can prove that the proposed and developed system can control the objects dynamically in a convenient fashion.

• 한국정보통신학회논문지
• /
• 제23권12호
• /
• pp.1528-1534
• /
• 2019

최근 들어, 3차원의 깊이 카메라의 대중화로 인해서 RGB 영상에서 수행되던 연구에 새로운 관심과 기회가 생겼지만 사람의 손 자세의 추정은 여전히 어려운 주제 중의 하나로 분류되고 있다. 본 논문에서는 다양하게 입력되는 3차원의 깊이 영상으로부터 사람의 손의 자세를 학습 알고리즘을 이용하여 강인하게 추정하는 방법을 제안한다. 제안된 접근 방법에서는 먼저 뼈대 기반의 손 모델을 생성한 다음, 생성된 손 모델을 3차원의 포인트 클라우드 데이터에 정렬한다. 그런 다음, 랜덤 포레스트 기반의 학습 알고리즘을 이용하여 정렬된 손 모델로부터 손의 자세를 강인하게 추정한다. 본 논문의 실험 결과에서는 제안된 접근 방법이 다양한 실내외의 환경에서 촬영된 입력 영상으로부터 사람의 손의 자세를 강인하고 빠르게 추정한다는 것을 보여준다.

• 로봇학회논문지
• /
• 제3권2호
• /
• pp.81-90
• /
• 2008

While increasing demand of the service for the disabled and the elderly people, assistive technologies have been developed rapidly. The natural signal of human such as voice or gesture has been applied to the system for assisting the disabled and the elderly people. As an example of such kind of human robot interface, the Soft Remote Control System has been developed by HWRS-ERC in $KAIST^[1]$. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and intra-person variation. Intra-person variation can be handled by inducing fuzzy concept. In this paper, we propose multivariate fuzzy decision tree(MFDT) learning and classification algorithm for hand motion recognition. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user's hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. For the performance of hand gesture recognition, we tested using hand gesture data which is collected from 10 people for 15 days. The experimental results show that the classification and user adaptation performance of proposed algorithm is better than general fuzzy decision tree.

• 대한인간공학회지
• /
• 제31권4호
• /
• pp.533-540
• /
• 2012

Objective: This paper targets a framework of a hand gesture based interface design. Background: While a modeling of contact-based interfaces has focused on users' ergonomic interface designs and real-time technologies, an implementation of a contactless interface needs error-free classifications as an essential prior condition. These trends made many research studies concentrate on the designs of feature vectors, learning models and their tests. Even though there have been remarkable advances in this field, the ignorance of ergonomics and users' cognitions result in several problems including a user's uneasy behaviors. Method: In order to incorporate compatibilities considering users' comfortable behaviors and device's classification abilities simultaneously, classification-oriented gestures are extracted using the suggested human-hand model and closed-loop classification procedures. Out of the extracted gestures, the compatibility-oriented gestures are acquired though human's ergonomic and cognitive experiments. Then, the obtained hand gestures are converted into a series of hand behaviors - Handycon - which is mapped into several functions in a mobile device. Results: This Handycon model guarantees users' easy behavior and helps fast understandings as well as the high classification rate. Conclusion and Application: The suggested framework contributes to develop a hand gesture-based contactless interface model considering compatibilities between human and device. The suggested procedures can be applied effectively into other contactless interface designs.

• 한국컴퓨터정보학회논문지
• /
• 제23권9호
• /
• pp.1-6
• /
• 2018

In this paper, we propose a method dividing effectively the hand and fingers using general webcam. The method executes 4 times empirically preprocessing one to erase noise. First, it erases the overall noise of the image using Gaussian smoothing. Second, it changes from RGB image to HSV color model and YCbCr color model, executes a global static binarization based on the statistical value for each color model, and erase the noise through bitwise-OR operation. Third, it executes outline approximation and inner region filling algorithm using RDP algorithm and Flood fill algorithm and erase noise. Lastly, it erases noise through morphological operation and determines the threshold propositional to the image size and selects the hand and fingers area. This paper compares to existing one color based hand area division method and focuses the noise deduction and can be used to a gesture recognition application.

• 한국신뢰성학회지:신뢰성응용연구
• /
• 제17권2호
• /
• pp.159-167
• /
• 2017

Purpose: The purpose of this research is to study research trend in the field of warranty and maintenance policy of second-hand products. Methods: To this end, we consider research articles, which deal with warranty and maintenance of the second-hand products, published on journals during the past 20 years and classify them by taxonomy scheme proposed by Shafiee and Chukova (2013). The taxonomy scheme consists of three maintenance models in warranty for second-hand product. In each models, we analyze proposed maintenance and warranty policies with respect to types of upgrade models, types of preventive maintenances, decision variables and decision criteria model. Results: We obtain the scheme of maintenance and warranty of the second-hand products and define cost related to warranty and maintenance of the second-hand item. Also, we summarize the characteristics of maintenance and warranty policies in each classified model. Conclusion: There have been several research reviews on maintenance and warranty polity of new products. This research surveys researches of authors during the past 20 years and classifies, summarizes and compares proposed maintenance and warranty policies of the second-hand products. This research provides useful information to researchers who are interested in maintenance and warranty of the second-hand products.

• 디지털융복합연구
• /
• 제15권8호
• /
• pp.391-400
• /
• 2017

본 논문의 목적은 영상자극이 신체 안정성에 미치는 영향을 개념 모델(Conceptual Model)로 제시하고, 시청자에게 제공되는 영상의 종류(2D, VR)에 따라 신체 안정성(손 안정성)에 미치는 영항을 실험을 통해 알아보는 것이다. 최근에 VR과 같은 입체 영상이 스마트폰이나 운동기구 등과의 결합이 활발해지고 있고, 영상자극이 제거된 후 일시적으로 신체 균형 및 손 안정성에 영향을 미침에 따라 안전사고나 인적오류의 가능성도 높아지고 있다. 개념모델은 기존 연구결과들을 바탕으로 제시되었고, 실험결과를 기반으로 뇌에서 일어나는 인간정보처리과정 및 인지적 자원모델과 결합되어 설명되었다. 20명의 피실험자는 2D, VR 자극에 노출 된 후 영상피로가 Cybersickness 질문지를 통해, 손 안정성은 Hand Steadiness Tester를 이용해 측정되었다. 실험결과 2D보다 VR 영상이 높은 영상피로, 낮은 손 안정성을 유발하였다. 본 연구는 아직 시도되지 않은 영상 종류 및 영상피로 수준에 따른 손 안정성을 개념모델과 실험을 통해 밝혔다는데 의의가 있다.

• 전기학회논문지
• /
• 제59권8호
• /
• pp.1477-1481
• /
• 2010

In this paper, we propose the classification and tracking method of the hand region using deformable template and condensation. To do this, first, we extract the hand region by using the fuzzy color filter and HCbCr color model. Second, we extract the edge of hand by applying the Canny edge algorithm. Third, we find the first template by calculating the conditional probability between the extracted edge and the model edge. If the accurate template of the first object is decided, the condensation algorithm tries to track it. Finally, we demonstrate the effectiveness and feasibility of the proposed method through some experiments.

• 한국멀티미디어학회논문지
• /
• 제11권6호
• /
• pp.807-815
• /
• 2008

A simple efficient method of spotting and recognizing hand gestures in video is presented using a network of hidden Markov models and dynamic programming search algorithm. The description starts from designing a set of isolated trajectory models which are stochastic and robust enough to characterize highly variable patterns like human motion, handwriting, and speech. Those models are interconnected to form a single big network termed a spotting network or a spotter that models a continuous stream of gestures and non-gestures as well. The inference over the model is based on dynamic programming. The proposed model is highly efficient and can readily be extended to a variety of recurrent pattern recognition tasks. The test result without any engineering has shown the potential for practical application. At the end of the paper we add some related experimental result that has been obtained using a different model - dynamic Bayesian network - which is also a type of stochastic model.