Wavelet Enhanced Image Preprocessing and Neural Networks for Hand Gesture Recognition

Fu, Xingang; Lu, Jiang; Zhang, Ting; Bonair, Chadwell; Coats, Marvin L.

doi:10.1109/smartcity.2015.172

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…The system could also extract statistical features of the wavelet coefficients. However, the conversion to 1-D conversion from 2-D affected the accuracy of neural network and thus, could be applied only to a few hand gestures [16].…”

Section: Related Workmentioning

confidence: 99%

Convolution neural networks for hand gesture recognation

Somanatti¹,

Patil²,

Hadimani³

2022

IJ-AI

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Hand gestures (not static or fixed positions) are movements of fingers and the arm to communicate messages. Hand gesture recognition is the process of identifying meaningful expressions involving the human hand. Pictorial representation of gestures will enable to understand human computer interaction (HCI). This paper describes a system using convolution neural network (CNN) for recognizing the 26 letters of the English alphabet signaled with hand gestures. A Python program was developed to recognize the gestures made in front of a web camera. The hand gestures obtained are categorized using CNN with a trained model. The model was constructed using 1,100 gestures images. The recognition rate was obtained with 91% of accuracy. The proposed method was found to be highly efficient in distinguishing and classifying gestures.

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Section: Related Workmentioning

confidence: 99%

Convolution neural networks for hand gesture recognation

Somanatti¹,

Patil²,

Hadimani³

2022

IJ-AI

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“…Hand gesture recognition approach based on image preprocessing using wavelet and supervised artificial neural networks is presented in [10]. The image preprocessing step processes the outline of the hand gesture.…”

Section: Related Workmentioning

confidence: 99%

Preprocessing to improve the accuracy of recognition of radioelement components in automated assembly devices

Semenishchev¹,

Zelensky²,

Zdanova³

et al. 2022

Optical Metrology and Inspection for Industrial Applications IX

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The article presumes a data processing algorithm that improves the accuracy of recognition of radio-electronic components in devices for automated installation. The paper proposes the use of a multicriteria filtering method that allows you to automatically change the smoothing coefficient. Varying the coefficient allows both reducing the noise component and preserving the boundaries of the radio elements without blurring. In order to enhance the contours of objects, a data simplification method is applied using the technique of reducing the range of clusters of color gradient histograms while preserving the shapes of objects. At the stage of detecting the boundaries of the elements and forming the structure of the elements of the radio component base, a modified one-dimensional two-criteria method is used. The combined analytical approach allows detection of the boundaries of radioelements and increases the productivity of the process. As test data used to evaluate the effectiveness, pairs of test images obtained by sensors fixed at various magnifications with a resolution of 1024x768 (8 bit, color image, visible range) are used. Images of simple shapes are used as analyzed objects.

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“…This conversion is depicted in Figure 6b. The approach is known in the signal processing community [31] and has also been carried over to data-driven models like Recursive Neural Nets [32], [33]. That said, it is still suffers from the same downsides as approaches 1 and 2.…”

Section: Response Variable Choicementioning

confidence: 99%

Operational Learning-based Boundary Estimation in Electromagnetic Medical Imaging

Al-Saffar,

Stancombe,

Zamani

et al. 2021

Preprint

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Incorporating boundaries of the imaging object as a priori information to imaging algorithms can significantly improve the performance of electromagnetic medical imaging systems. To avoid overly complicating the system by using different sensors and the adverse effect of the subject's movement, a learning-based method is proposed to estimate the boundary (external contour) of the imaged object using the same electromagnetic imaging data. While imaging techniques may discard the reflection coefficients for being dominant and uninformative for imaging, these parameters are made use of for boundary detection. The learned model is verified through independent clinical human trials by using a head imaging system with a 16-element antenna array that works across the band 0.7-1.6 GHz. The evaluation demonstrated that the model achieves average dissimilarity of 0.012 in Hu-moment while detecting head boundary. The model enables fast scan and image creation while eliminating the need for additional devices for accurate boundary estimation.

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Wavelet Enhanced Image Preprocessing and Neural Networks for Hand Gesture Recognition

Cited by 11 publications

References 18 publications

Convolution neural networks for hand gesture recognation

Convolution neural networks for hand gesture recognation

Preprocessing to improve the accuracy of recognition of radioelement components in automated assembly devices

Operational Learning-based Boundary Estimation in Electromagnetic Medical Imaging

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