126,757 research outputs found
A Real Time System for Hand Gesture Recognition
In this paper we explore the various aspects of hand gesture recognition in real time using neural networks. Hand gesture can be a vital way for the user to interact with any system. In this system we capture a hand gesture from the user and then perform the action related to it. This provides us with an alternative to mouse and keyboard to control a system. Hand gesture recognition can be helpful in various fields and areas where interacting with the system without touch is important. Hand gesture recognition is incorporated along with image processing and to add additional accuracy we are using neural network. This combination of image processing and neural network in real time forms a really powerful tool, forming the base of our project
Hand Gesture Recognition as Password to Open the Door with Camera and Convexity Defect Method
Computer Vision is one of reasearch that gets a lot of attention with many applications. One of the application is the hand gesture recognition system. By using EmguCV, will be obtained camera images from webcam camera. The Pictures will be disegmented by using skin detection method for decrease noises in order to obtain the information needed. The final project of this system is to implement the convexity defect method for extracting images and recognize patterns of hand gesture that represent the characters A, B, C, D, and E. The parameters used in pattern recognition of hand gesture is the number and length of the line connecting the hull and defects derived from the pattern of hand gesture
Hand Gesture Recognition as Password to Open The Door With Camera and Convexity Defect Method
Computer Vision is one of reasearch that gets a lot of attention with many applications. One of the application is the hand gesture recognition system. By using EmguCV, will be obtained camera images from webcam camera. The Pictures will be disegmented by using skin detection method for decrease noises in order to obtain the information needed. The final project of this system is to implement the convexity defect method for extracting images and recognize patterns of hand gesture that represent the characters A, B, C, D, and E. The parameters used in pattern recognition of hand gesture is the number and length of the line connecting the hull and defects derived from the pattern of hand gesture
A Collaborative Augmented Reality System Based On Real Time Hand Gesture Recognition
Human computer interaction is a major issue in research industry. In order to offer a way to enable untrained users to interact with computer more easily and efficiently gesture based interface has been paid more attention. Gesture based interface provides the most effective means for non-verbal interaction. Various devices like head mounted display and hand glove could be used by the user but they may be cumbersome to use and they limits the user action and make them tired. This problem can be solved by the real time bare hand gesture recognition technique for human computer interaction using computer vision Computer vision is becoming very popular now a days since it can hold a lot of information at a very low cost. With this increasing popularity of computer vision there is a rapid development in the field of virtual reality as it provides an easy and efficient virtual interface between human and computer. At the same time much research is going on to provide more natural interface for human-computer interaction with the power of computer vision .The most powerful and natural interface for human-computer interaction is the hand gesture. In this project we focus our attention to vision based recognition of hand gesture for personal authentication where hand gesture is used as a password. Different hand gestures are used as password for different personals
Hand Tracking and Gesture Recognition for Human-Computer Interaction
The proposed work is part of a project that aims for the control of a videogame based on hand gesture recognition. This goal implies the restriction of real-time response and unconstrained environments. In this paper we present a real-time algorithm to track and recognise hand gestures for interacting with the videogame. This algorithm is based on three main steps: hand segmentation, hand tracking and gesture recognition from hand features. For the hand segmentation step we use the colour cue due to the characteristic colour values of human skin, its invariant properties and its computational simplicity. To prevent errors from hand segmentation we add a second step, hand tracking. Tracking is performed assuming a constant velocity model and using a pixel labeling approach. From the tracking process we extract several hand features that are fed to a finite state classifier which identifies the hand configuration. The hand can be classified into one of the four gesture classes or one of the four different movement directions. Finally, using the system's performance evaluation results we show the usability of the algorithm in a videogame environment
A Novel Approach for Operating Electrical Appliances Using Hand Gesture Recognition
Vision-based automatic hand gesture acknowledgement has been a very active research theme in recent years with inspiring applications such as human computer interaction (HCI), electronics device command, and signal language understanding. Hand sign recognition is presented through a curvature space procedure in which finding the boundary contours of the hand are engaged. This is a robust approach that is scale, translation and rotation invariant on the hand poses yet it is computationally demanding. A method for signal acknowledgement for signal language understanding has been proposed in computer vision. Human interaction involves various hand processing task like hand detection, recognition and hand tracking. This technology mainly focuses on the needs of physically challenged group of people and helps them to operate just by showing hand gestures. Thus, our project is aimed at making a system that could recognized human gesture through computer vision
Hand gesture based digit recognition
Recognition of static hand gestures in our daily plays an important role in human-computer interaction. Hand gesture recognition has been a challenging task now a days so a lot of research topic has been going on due to its increased demands in human computer interaction. Since Hand gestures have been the most natural communication medium among human being, so this facilitate efficient human computer interaction in many electronics gazettes . This has led us to take up this task of hand gesture recognition. In this project different hand gestures are recognized and no of fingers are counted. Recognition process involve steps like feature extraction, features reduction and classification. To make the recognition process robust against varying illumination we used lighting compensation method along with YCbCr model. Gabor filter has been used for feature extraction because of its special mathematical properties. Gabor based feature vectors have high dimension so in our project 15 local gabor filters are used instead of 40 Gabor filters. The objective in using fifteen Gabor filters is used to mitigate the complexity with improved accuracy. In this project the problem of high dimensionality of feature vector is being solved by using PCA. Using local Gabor filter helps in reduction of data redundancy as compared to that of 40 filters. Classification of the 5 different gestures is done with the use of one against all multiclass SVM which is also compared with Euclidean distance and cosine similarity while the former giving an accuracy of 90.86%
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