The Experts below are selected from a list of 24852 Experts worldwide ranked by ideXlab platform
Sameem Abdulkareem - One of the best experts on this subject based on the ideXlab platform.
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retracted article static Hand Gesture recognition using neural networks
Artificial Intelligence Review, 2014Co-Authors: Haitham Hasan, Sameem AbdulkareemAbstract:This paper presents a novel technique for Hand Gesture recognition through human---computer interaction based on shape analysis. The main objective of this effort is to explore the utility of a neural network-based approach to the recognition of the Hand Gestures. A unique multi-layer perception of neural network is built for classification by using back-propagation learning algorithm. The goal of static Hand Gesture recognition is to classify the given Hand Gesture data represented by some features into some predefined finite number of Gesture classes. The proposed system presents a recognition algorithm to recognize a set of six specific static Hand Gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The Hand Gesture image is passed through three stages, preprocessing, feature extraction, and classification. In preprocessing stage some operations are applied to extract the Hand Gesture from its background and prepare the Hand Gesture image for the feature extraction stage. In the first method, the Hand contour is used as a feature which treats scaling and translation of problems (in some cases). The complex moment algorithm is, however, used to describe the Hand Gesture and treat the rotation problem in addition to the scaling and translation. The algorithm used in a multi-layer neural network classifier which uses back-propagation learning algorithm. The results show that the first method has a performance of 70.83% recognition, while the second method, proposed in this article, has a better performance of 86.38% recognition rate.
Zhengyou Zhang - One of the best experts on this subject based on the ideXlab platform.
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robust Hand Gesture recognition with kinect sensor
ACM Multimedia, 2011Co-Authors: Jingjing Meng, Junsong Yuan, Zhengyou ZhangAbstract:Hand Gesture based Human-Computer-Interaction (HCI) is one of the most natural and intuitive ways to communicate between people and machines, since it closely mimics how human interact with each other. In this demo, we present a Hand Gesture recognition system with Kinect sensor, which operates robustly in uncontrolled environments and is insensitive to Hand variations and distortions. Our system consists of two major modules, namely, Hand detection and Gesture recognition. Different from traditional vision-based Hand Gesture recognition methods that use color-markers for Hand detection, our system uses both the depth and color information from Kinect sensor to detect the Hand shape, which ensures the robustness in cluttered environments. Besides, to guarantee its robustness to input variations or the distortions caused by the low resolution of Kinect sensor, we apply a novel shape distance metric called Finger-Earth Mover's Distance (FEMD) for Hand Gesture recognition. Consequently, our system operates accurately and efficiently. In this demo, we demonstrate the performance of our system in two real-life applications, arithmetic computation and rock-paper-scissors game.
Jingjing Meng - One of the best experts on this subject based on the ideXlab platform.
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robust Hand Gesture recognition with kinect sensor
ACM Multimedia, 2011Co-Authors: Jingjing Meng, Junsong Yuan, Zhengyou ZhangAbstract:Hand Gesture based Human-Computer-Interaction (HCI) is one of the most natural and intuitive ways to communicate between people and machines, since it closely mimics how human interact with each other. In this demo, we present a Hand Gesture recognition system with Kinect sensor, which operates robustly in uncontrolled environments and is insensitive to Hand variations and distortions. Our system consists of two major modules, namely, Hand detection and Gesture recognition. Different from traditional vision-based Hand Gesture recognition methods that use color-markers for Hand detection, our system uses both the depth and color information from Kinect sensor to detect the Hand shape, which ensures the robustness in cluttered environments. Besides, to guarantee its robustness to input variations or the distortions caused by the low resolution of Kinect sensor, we apply a novel shape distance metric called Finger-Earth Mover's Distance (FEMD) for Hand Gesture recognition. Consequently, our system operates accurately and efficiently. In this demo, we demonstrate the performance of our system in two real-life applications, arithmetic computation and rock-paper-scissors game.
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Depth camera based Hand Gesture recognition and its applications in Human-Computer-Interaction
2011 8th International Conference on Information Communications & Signal Processing, 2011Co-Authors: Ren Zhou, Yuan Junsong, Zhou Ren, Meng Jingjing, Jingjing Meng, Junsong YuanAbstract:Of various Human-Computer-Interactions (HCI), Hand Gesture based HCI might be the most natural and intuitive way to communicate between people and machines, since it closely mimics how human interact with each other. Its intuitiveness and naturalness have spawned many applications in exploring large and complex data, computer games, virtual reality, health care, etc. Although the market for Hand Gesture based HCI is huge, building a robust Hand Gesture recognition system remains a challenging problem for traditional vision-based approaches, which are greatly limited by the quality of the input from optical sensors. [16] proposed a novel dissimilarity distance metric for Hand Gesture recognition using Kinect sensor, called Finger-Earth Mover's Distance (FEMD). In this paper, we compare the performance in terms of speed and accuracy between FEMD and traditional corresponding-based shape matching algorithm, Shape Context. And then we introduce several HCI applications built on top of a accurate and robust Hand Gesture recognition system based on FEMD. This Hand Gesture recognition system performs robustly despite variations in Hand orientation, scale or articulation. Moreover, it works well in uncontrolled environments with background clusters. We demonstrate that this robust Hand Gesture recognition system can be a key enabler for numerous Hand Gesture based HCI systems.
Haitham Hasan - One of the best experts on this subject based on the ideXlab platform.
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retracted article static Hand Gesture recognition using neural networks
Artificial Intelligence Review, 2014Co-Authors: Haitham Hasan, Sameem AbdulkareemAbstract:This paper presents a novel technique for Hand Gesture recognition through human---computer interaction based on shape analysis. The main objective of this effort is to explore the utility of a neural network-based approach to the recognition of the Hand Gestures. A unique multi-layer perception of neural network is built for classification by using back-propagation learning algorithm. The goal of static Hand Gesture recognition is to classify the given Hand Gesture data represented by some features into some predefined finite number of Gesture classes. The proposed system presents a recognition algorithm to recognize a set of six specific static Hand Gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The Hand Gesture image is passed through three stages, preprocessing, feature extraction, and classification. In preprocessing stage some operations are applied to extract the Hand Gesture from its background and prepare the Hand Gesture image for the feature extraction stage. In the first method, the Hand contour is used as a feature which treats scaling and translation of problems (in some cases). The complex moment algorithm is, however, used to describe the Hand Gesture and treat the rotation problem in addition to the scaling and translation. The algorithm used in a multi-layer neural network classifier which uses back-propagation learning algorithm. The results show that the first method has a performance of 70.83% recognition, while the second method, proposed in this article, has a better performance of 86.38% recognition rate.
Liu Chong-qing - One of the best experts on this subject based on the ideXlab platform.
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Hand Gesture Recognition Based on Fourier Descriptors with Complex Backgrounds
Computer Simulation, 2005Co-Authors: Liu Chong-qingAbstract:Hand Gesture is one of the most popular communication methods in everyday life.Hand Gesture recognition research has gained a lot of attentions because of its applications for interactive human-machine interface and virtual environments. But currently, in the vision-based Hand Gesture recognition, almost all the technologies on Hand Gesture segmentation are based on simple background or on gloves in special colors. However, this paper presents a method that segments the Hand Gestures with complex backgrounds through the combination of motion and skin color based on KL Transformation,in contrast with traditional Hand Gesture segmentation based on RGB color model in some environments.After the pretreatment to Hand Gesture region, we use a normalized Fourier descriptor, which is more accurate than the traditional Fourier descriptor,to select the Hand Gesture features and use the traditional 3 levels BP network to perform Hand Gesture recognition.Finally, the average recognition rate is 95.9% on the training set and 95% on the testing set.
