Vision based Real Time Hand Gesture Recognition Techniques for Human Computer Interaction

Ghotkar, Archana; Kharate, G. K.

doi:10.5120/12148-8103

Cited by 32 publications

(33 citation statements)

References 15 publications

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“…Next, we tested the accuracy of our system's event detection. We use our system to process our test dataset and compare with the performances of the state of the art approaches by Bao et al [19] and Ghotkar et al [20] respectively. The overall performance comparison is given in table 4, where recall and precision are used to evaluate the recognition performance.…”

Section: B Designing the Ontology Of Gesturesmentioning

confidence: 99%

Human action recognition using a semantic-probabilistic network

Kovalenko

Antoshchuk

Sieck

2015

2015 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)

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In this paper we propose a semantic-probabilistic network to recognise human actions. We use a predefined domain ontology to describe the events and scenarios in the scene as a hierarchical decomposition of simple concepts and variables and then perform an automated conversion of the ontology into a Bayesian network. A novel approach for Bayesian network nodes' weights calculation is introduced based on the weighted relation between concepts of the ontology in order to reduce the influence of incorrect object detection. We then evaluate the performance of our approach using it to predict gestures in a human gesture recognition system, using a set of pre-recorded video sequences.

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Section: B Designing the Ontology Of Gesturesmentioning

confidence: 99%

Human action recognition using a semantic-probabilistic network

Kovalenko

Antoshchuk

Sieck

2015

2015 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)

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“…If the color of a pixel falls into the "skin" region, it is classified as "skin" and vice versa. Some studies that applied the thresholding method to skin detection can be referred to as in [14,[17][18][19][20]. In short, the thresholding method gains an advantage because it is a very basic and understandable method; however, it is mainly based on subjective experience and has low performance when the thresholds are incorrectly tuned [1,21].…”

Section: Introductionmentioning

confidence: 99%

A New Efficient Approach to Detect Skin in Color Image Using Bayesian Classifier and Connected Component Algorithm

Nguyen‐Trang

2018

Mathematical Problems in Engineering

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Skin detection is an interesting problem in image processing and is an important preprocessing step for further techniques like face detection, objectionable image detection, etc. However, its performance has not really been high because of the high overlapped degree between "skin" and "nonskin" pixels. This paper proposes a new approach to improve the skin detection performance using the Bayesian classifier and connected component algorithm. Specifically, the Bayesian classifier is utilized to identify "true skin" pixels using the first posterior probability threshold, which is approximate to 1, and to identify "skin candidate" pixels using the second posterior probability threshold. Subsequently, the connected component algorithm is used to find all the connected components containing the "skin candidate" pixels. According to the fact that a skin pixel often connects with other skin pixels in an image, all pixels in a connected component are classified as "skin" if there is at least one "true skin" pixel in that connected component. It means that the "nonskin" pixels whose color is similar to skin are classified as "nonskin" when they have the posterior probabilities lower than the first posterior probability threshold and do not connect with any "true skin" pixel. This idea can help us to improve the skin classification performance, especially the false positive rate.

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“…Segmentation by combining more than one color model improves the accuracy of skin detection. Ghotkar and Kharate [6] described a hand segmentation method by using a threshold technique for hand gesture recognition. They made a comparison between three color spaces (HSV, HSL, and HTS) and found that the last one gave better results than the others.…”

Section: Related Workmentioning

confidence: 99%

Skin Segmentation Using YUV and RGB Color Spaces

Al-Tairi¹,

Rahmat²,

Saripan³

et al. 2014

Journal of Information Processing Systems

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Abstract-Skin detection is used in many applications, such as face recognition, hand tracking, and human-computer interaction. There are many skin color detection algorithms that are used to extract human skin color regions that are based on the thresholding technique since it is simple and fast for computation. The efficiency of each color space depends on its robustness to the change in lighting and the ability to distinguish skin color pixels in images that have a complex background. For more accurate skin detection, we are proposing a new threshold based on RGB and YUV color spaces. The proposed approach starts by converting the RGB color space to the YUV color model. Then it separates the Y channel, which represents the intensity of the color model from the U and V channels to eliminate the effects of luminance. After that the threshold values are selected based on the testing of the boundary of skin colors with the help of the color histogram. Finally, the threshold was applied to the input image to extract skin parts. The detected skin regions were quantitatively compared to the actual skin parts in the input images to measure the accuracy and to compare the results of our threshold to the results of other's thresholds to prove the efficiency of our approach. The results of the experiment show that the proposed threshold is more robust in terms of dealing with the complex background and light conditions than others.

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Vision based Real Time Hand Gesture Recognition Techniques for Human Computer Interaction

Cited by 32 publications

References 15 publications

Human action recognition using a semantic-probabilistic network

Human action recognition using a semantic-probabilistic network

A New Efficient Approach to Detect Skin in Color Image Using Bayesian Classifier and Connected Component Algorithm

Skin Segmentation Using YUV and RGB Color Spaces

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