Gesture Recognition Module Research Articles

Automatic user state recognition for hand gesture based low-cost television control system

Hand gesture based television (TV) control is attracting more and more researchers. Most of existing works focus on the hand gesture recognition algorithm and the corresponding user interface, while the power consumption or computational cost is not considered carefully. In practice, keeping the camera device and the gesture recognition module running all the time often costs much energy. Till now, few methods have been reported to solve this problem. This paper proposes an automatic user state recognition scheme to recognize the TV user's state and activate the camera-based gesture recognition module only when the user is trying to control the TV. Specifically, the user's behavior active or not is detected by lowcost sensors, the user's gaze watching TV or not is tracked by the face-based view detection, and the user's state is then decided according to a finite-state machine composed of four states: Absent, OtherAction, Controlling, and Watching. The prototypes based on an ultrasonic distance sensor array, a red-green-blue (RGB) camera, and a depth camera are implemented and tested. The results show that the proposed scheme can effectively reduce the power consumption or computational cost of the original hand gesture based control schemes.

A Multilayer Hidden Markov Models-Based Method for Human-Robot Interaction

To achieve Human-Robot Interaction (HRI) by using gestures, a continuous gesture recognition approach based on Multilayer Hidden Markov Models (MHMMs) is proposed, which consists of two parts. One part is gesture spotting and segment module, the other part is continuous gesture recognition module. Firstly, a Kinect sensor is used to capture 3D acceleration and 3D angular velocity data of hand gestures. And then, a Feed-forward Neural Networks (FNNs) and a threshold criterion are used for gesture spotting and segment, respectively. Afterwards, the segmented gesture signals are respectively preprocessed and vector symbolized by a sliding window and a K-means clustering method. Finally, symbolized data are sent into Lower Hidden Markov Models (LHMMs) to identify individual gestures, and then, a Bayesian filter with sequential constraints among gestures in Upper Hidden Markov Models (UHMMs) is used to correct recognition errors created in LHMMs. Five predefined gestures are used to interact with a Kinect mobile robot in experiments. The experimental results show that the proposed method not only has good effectiveness and accuracy, but also has favorable real-time performance.

웹캠을 이용한 동적 제스쳐 인식 기반의 감성 메신저 구현 및 성능 분석

본 논문에서는 웹캠을 이용하여 사용자의 안면 또는 손동작을 인식하고, 그 제스쳐가 나타내는 감성(희노애락)을 플래시 콘으로 표현하여 상대방에게 전송하는 감성 메신저를 구현한다. 이 메신저는 안면 인식 모듈과 손동작 인식 모듈, 메신저 모듈로 구성된다. 안면 인식 모듈에서는 눈, 입의 각 영역을 이진 영상으로 변환하여 눈과 입의 모양 변화에 따라 윙크, 입맞춤, 하품 등을 인식한다. 또한 손동작 인식 모듈에서는 인식한 손가락 수에 따라 가위-바위-보로 인식한다. 메신저 모듈은 안면 인식 모듈과 손동작 인식 모듈에서 인식한 윙크, 입맞춤, 하품과 가위-바위-보를 플래시 콘으로 표현하여 상대방에게 전달한다. 본 논문에서는 시뮬레이션을 통하여 감성 메신저의 CPU 점유율이 최소임을 검증한다. 또한 감성 메신저의 손동작 인식 모듈의 성능이 안면 인식 모듈에 비해 우수함을 보인다. In this paper, we propose an emotion messenger which recognizes face or hand gestures of a user using a WebCAM, converts recognized emotions (joy, anger, grief, happiness) to flash-cones, and transmits them to the counterpart. This messenger consists of face recognition module, hand gesture recognition module, and messenger module. In the face recognition module, it converts each region of the eye and the mouth to a binary image and recognizes wink, kiss, and yawn according to shape change of the eye and the mouth. In hand gesture recognition module, it recognizes gawi-bawi-bo according to the number of fingers it has recognized. In messenger module, it converts wink, kiss, and yawn recognized by the face recognition module and gawi-bawi-bo recognized by the hand gesture recognition module to flash-cones and transmits them to the counterpart. Through simulation, we confirmed that CPU share ratio of the emotion messenger is minimized. Moreover, with respect to recognition ratio, we show that the hand gesture recognition module performs better than the face recognition module.

From virtual slides to virtual pathology institution: the future of tissue-based diagnosis?

In this paper, we describe an augmented reality tool for collaborative work called the MagicBoard. The MagicBoard is based on an ordinary white board which has been enhanced by a video-projector and a steerable camera. A supervisor coordinates the cooperation of several modules including gesture recognition, finger tracking and white board scanning for digitalisation of the content. The gesture recognition module uses an approach based on local spatio-temporal appearance of activities. The tracking module is designed for use with cluttered backgrounds and variable lighting conditions. The white board scanner eliminates global luminosity differences by adaptive thresholding and the result can serve to digitise the content of the board. The supervisor is based on a rule-based architecture and is easily extendable. The selected modules fit together to a compact system, that largely increases the functionality of a white board and makes it a useful tool in the future office environments.

MagicBoard: A contribution to an intelligent office environment

In this paper, we describe an augmented reality tool for collaborative work called the MagicBoard. The MagicBoard is based on an ordinary white board which has been enhanced by a video-projector and a steerable camera. A supervisor coordinates the cooperation of several modules including gesture recognition, finger tracking and white board scanning for digitalisation of the content. The gesture recognition module uses an approach based on local spatio-temporal appearance of activities. The tracking module is designed for use with cluttered backgrounds and variable lighting conditions. The white board scanner eliminates global luminosity differences by adaptive thresholding and the result can serve to digitise the content of the board. The supervisor is based on a rule-based architecture and is easily extendable. The selected modules fit together to a compact system, that largely increases the functionality of a white board and makes it a useful tool in the future office environments.