Background invariant static hand gesture recognition based on Hidden Markov Models

Vieriu, Radu-Laurentiu; Mironica, Ionut; Goras, Bogdan-Tudor

doi:10.1109/isscs.2013.6651245

Cited by 9 publications

(4 citation statements)

References 21 publications

(18 reference statements)

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“…However, the method is unreliable because of the difficulty to have a model for skin colour that can represent people of different ethnicity and can be applicable under various lighting conditions. Vieriu et al (2013) proposed a simple system that is developed by utilizing artificial neural network and skin colour segmentation. However, the extracted features resulting from these methods do not have sufficient hand shape information, which is a key attribute that has a bearing on the recognition accuracy.…”

Section: State Of the Artmentioning

confidence: 99%

Static Hand Gestures: Fingertips Detection Based on Segmented Images

Dawod¹,

Nordin²,

Abdullah³

2015

Journal of Computer Science

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Fingertips detection is important for recognition of static gesture in sign language. This paper presents a new method that is based on YCbCr colour space and skeletonization for fingertips detection towards gesture segmentation and recognition. The method begins with the conversion of an image in RGB colour space into YCbCr colour space. Then, the chrominance Cb and Cr are extracted from the YCbCr colour space. A thresholding technique, which is based on a pre-defined range for Cb and Cr components representing the skin colour value is used to extract the hand from the background and to achieve the binary image. A morphological processing is performed on the obtained binary image to remove noise and unwanted image pixels. The candidate fingertips position is then calculated based on skeletonization algorithm and tracing process. The centroid is subsequently found, after which the Euclidean distances between the pixels' coordinates that belong to the candidate fingertips and the centroid are calculated towards validating their representation of the actual fingertips. Based on the proposed method, the fingertips of twenty-six American Sign Language (ASL) alphabets hand sign samples were detected successfully and the gesture was correctly recognized with an accuracy of 96.3% during the conducted experiments.

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Section: State Of the Artmentioning

confidence: 99%

Static Hand Gestures: Fingertips Detection Based on Segmented Images

Dawod¹,

Nordin²,

Abdullah³

2015

Journal of Computer Science

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“…Figure 4 shows the captured image of said gestures in RGB colorspace. For simplicity, we used simple segmentation technique based on computing maximum and minimum skin probabilities of input RGB image as suggested by author in [10]. Figure 5 shows the result images obtained after segmentation.…”

Section: Recognition (Hgr) Is a Branch Of Human Computermentioning

confidence: 99%

Vision based hand gesture recognition using eccentric approach for human computer interaction

Bhame¹,

Sreemathy²,

Dhumal³

2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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There has been growing interest in the development of new approaches and technologies for bridging the human computer barrier. Hand gesture recognition is considered as an interaction technique having potential to communicate with machines. Human computer interaction (Hel) was never an easy task and lots of approaches are available to build such systems.Hand gesture recognition (HGR) using wearable data glove provides a solution to build a HeI system, but lags in terms of its computational time and poor interface. Pattern matching is one more solution which uses vision based techniques and provides strong interface to build Hel systems. But again, it requires complex algorithms which takes lots of computational time and hence limits its use in real time HeI applications. In this paper, we presented an eccentric approach for hand gesture recognition which is simple, fast and user independent and can be used to develop real time Hel applications. Based on proposed algorithm we built a system for Indian Sign Language recognition which converts Indian Sign numbers into text. The algorithm first captures the image of single handed gesture of speech/hearing impaired person using simple webcam and then using our proposed algorithm it classifies the gesture into its appropriate class. It uses simple logical conditions for gesture classification which make its use in real time HeI applications.

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“…HMM often used in various applications, an effective learning algorithm, and can handle variations in record structure [2]. Referring to the research [17], a static hand gesture recognition using the HMM has an average accuracy rate of 93.38%.…”

Section: Introductionmentioning

confidence: 99%

Hand gesture recognition using discrete wavelet transform and hidden Markov models

2020

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Gesture recognition based on computer-vision is an important part of human-computer interaction. But it lacks in several points, that was image brightness, recognition time, and accuracy. Because of that goal of this research was to create a hand gesture recognition system that had good performances using discrete wavelet transform and hidden Markov models. The first process was pre-processing, which done by resizing the image to 128x128 pixels and then segmented the skin color. The second process was feature extraction using the discrete wavelet transform. The result was the feature value in the form of a feature vector from the image. The last process was gesture classification using hidden Markov models to calculate the highest probability of feature matrix which had obtained from the feature extraction process. The result of the system had 72% of accuracy using 150 training and 100 test data images that consist five gestures. The newness thing found in this experiment were the effect of acquisition and pre-processing. The accuracy had been escalated by 14% compared to Sebastien's dataset at 58%. The increment effect propped by brightness and contrast value.

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Background invariant static hand gesture recognition based on Hidden Markov Models

Cited by 9 publications

References 21 publications

Static Hand Gestures: Fingertips Detection Based on Segmented Images

Static Hand Gestures: Fingertips Detection Based on Segmented Images

Vision based hand gesture recognition using eccentric approach for human computer interaction

Hand gesture recognition using discrete wavelet transform and hidden Markov models

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