Static hand gesture recognition based on HOG characters and support vector machines

Feng, Kun; Fang, Yuan

doi:10.1109/imsna.2013.6743432

Cited by 50 publications

(13 citation statements)

References 2 publications

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“…Pose and gesture classification is the last but somehow most determining step in recognition. It can be resolved by many popular artificial intelligence and machine learning algorithms including K-nearest neighbors [90,91], hidden Markov model [92,93], support vector machines [94][95][96][97], artificial neural networks [98][99][100], and deep learning algorithms, from which currently two methods are popular, namely convolutional neural networks and recurrent neural networks [101].…”

Section: Pose and Gesture Recognitionmentioning

confidence: 99%

CogniViTra, a Digital Solution to Support Dual-Task Rehabilitation Training

et al. 2021

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This article focuses on an eHealth application, CogniViTra, to support cognitive and physical training (i.e., dual-task training), which can be done at home with supervision of a health care provider. CogniViTra was designed and implemented to take advantage of an existing Platform of Services supporting a Cognitive Health Ecosystem and comprises several components, including the CogniViTra Box (i.e., the patient terminal equipment), the Virtual Coach to provide assistance, the Game Presentation for the rehabilitation exercises, and the Pose and Gesture Recognition to quantify responses during dual-task training. In terms of validation, a functional prototype was exposed in a highly specialized event related to healthy and active ageing, and key stakeholders were invited to test it and share their insights. Fifty-seven specialists in information-technology-based applications to support healthy and active ageing were involved and the results and indicated that the functional prototype presents good performance in recognizing poses and gestures such as moving the trunk to the left or to the right, and that most of the participants would use or suggest the utilization of CogniViTra. In general, participants considered that CogniViTra is a useful tool and may represent an added value for remote dual-task training.

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Section: Pose and Gesture Recognitionmentioning

confidence: 99%

CogniViTra, a Digital Solution to Support Dual-Task Rehabilitation Training

et al. 2021

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“…Figure 7 exposes the DM kNN architecture combined with FoV. The two image inputs are used as inputs through the popular histogram of oriented gradient (HOG) approach, the centroid value of each target is obtained [26].…”

Section: ) Target Localizationmentioning

confidence: 99%

Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning

et al. 2021

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“…Comparative analysis of newly proposed method with wavelet transform, Curvelet, Gabor filter, Legendre moments, Zernike, Gaussian Hermite moment, Histogram of Oriented Gradient (HOG) [24]were carried out in both the data set. In case of GMA no preprocessing is applied for recognition of both datasets but in case of other methods Grantha images are binarized using sauvola method before applying the concerned algorithm.…”

Section: Evalution Of Gma On Hpl and Grantha Datasetmentioning

confidence: 99%

Handwritten Character Recognition from Ancient Palm Leaves using Gabor based MultiLayer Architecture:GMA

L¹,

Rahiman²

2021

International Journal of Applied Engineering Research

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Feature extraction plays the key role in pattern recognition systems. With the invent of Deep learning algorithms it is believed that the importance of feature extraction methods has been reduced. But the cost of implementing deep algorithms is very high. Deep neural networks rely on GPU architecture and requires large amount of data for achieving high recognition efficiency. It is computationally expensive to train the deep architecture and more over the learning procedure and factors for training is not easy to realize. Therefore having inspired by the structure of Deep Convolutional Neural Network a new feature extraction method based on conventional feature extraction system for recognition is proposed. In this method a multilayer architecture is designed with convolution layer based on gabor filter and classification layer based on Artificial Neural Network.Only the classification layer is subjected to learning by backpropogation and all other layers acts as the part of feature extraction system.The input image without applying any pre-processing can be subjected to the proposed system which in turn predicts the class of image as output. The proposed method is compared with some of the existing efficient feature extraction methods like discrete meyer wavelet, zernike moment, curvelet , legendre moments, gaussian hermite(GH) moment and Histogram of gradient(HOG). The recognition efficiency produced by the method without applying any pre-processing on input images is much higher than existing efficient feature extraction methods with preprocessing applied.The proposed method works effectively invariant to noise, translation and rotation. Experimental analyses were carried out in two datasets. First dataset is the standard HPL dataset of isolated Tamil characters. The second dataset consists of Grantha characters extracted from ancient palm leaves.

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Static hand gesture recognition based on HOG characters and support vector machines

Cited by 50 publications

References 2 publications

CogniViTra, a Digital Solution to Support Dual-Task Rehabilitation Training

CogniViTra, a Digital Solution to Support Dual-Task Rehabilitation Training

Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning

Handwritten Character Recognition from Ancient Palm Leaves using Gabor based MultiLayer Architecture:GMA

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