Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

Huu, Phat Nguyen; Ngoc, Tan Phung

doi:10.1155/2021/3986497

Cited by 15 publications

(6 citation statements)

References 29 publications

(30 reference statements)

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“…HOG feature combined with a SVM classifier has been widely used in image recognition, especially in pedestrian detection [ 26 ]. It operates on the local grid cell of the image, which enables it to maintain a good invariance to the geometric and optical deformation of the image [ 27 ]. Since there is large randomness of viewing angles from the process of creating histopathology images, the HOG feature is particularly suitable for the feature extraction of histopathology images.…”

Section: Methodsmentioning

confidence: 99%

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Renal Cancer Detection: Fusing Deep and Texture Features from Histopathology Images

Cai

Liu

Zhang

et al. 2022

BioMed Research International

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Histopathological images contain morphological markers of disease progression that have diagnostic and predictive values, with many computer-aided diagnosis systems using common deep learning methods that have been proposed to save time and labour. Even though deep learning methods are an end-to-end method, they perform exceptionally well given a large dataset and often show relatively inferior results for a small dataset. In contrast, traditional feature extraction methods have greater robustness and perform well with a small/medium dataset. Moreover, a texture representation-based global approach is commonly used to classify histological tissue images expect in explicit segmentation to extract the structure properties. Considering the scarcity of medical datasets and the usefulness of texture representation, we would like to integrate both the advantages of deep learning and traditional machine learning, i.e., texture representation. To accomplish this task, we proposed a classification model to detect renal cancer using a histopathology dataset by fusing the features from a deep learning model with the extracted texture feature descriptors. Here, five texture feature descriptors from three texture feature families were applied to complement Alex-Net for the extensive validation of the fusion between the deep features and texture features. The texture features are from (1) statistic feature family: histogram of gradient, gray-level cooccurrence matrix, and local binary pattern; (2) transform-based texture feature family: Gabor filters; and (3) model-based texture feature family: Markov random field. The final experimental results for classification outperformed both Alex-Net and a singular texture descriptor, showing the effectiveness of combining the deep features and texture features in renal cancer detection.

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Section: Methodsmentioning

confidence: 99%

“…HOG feature extraction steps are as shown [ 27 ]. Normalize the image: convert the input image to a grayscale image and use the Gamma filter method to perform global normalization on the grayscale image.…”

Section: Methodsmentioning

confidence: 99%

Renal Cancer Detection: Fusing Deep and Texture Features from Histopathology Images

Cai

Liu

Zhang

et al. 2022

BioMed Research International

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“…ANN-based gesture recognition algorithm is an adaptive machine learning algorithm that learns several times based on some learning criterion to gradually improve the gesture recognition rate. In the literature, [32][33][34][35][36] authors have used this type of algorithm to control the movements of the robot manipulator. In this way, a model is first trained by a neural network, then human arm or palm data are used as input to the network, and the trained network outputs the corresponding result as control signals.…”

Section: Introductionmentioning

confidence: 99%

An interactive gesture control system for collaborative manipulator based on Leap Motion Controller

Li,

Lou,

Cai

et al. 2024

Advances in Mechanical Engineering

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Gesture control is often used for the control of robotic manipulators. However conventional methods always focus on position control of robotic manipulators and use a fixed position to place gesture detection devices, which limits the flexibility to control and interact. This paper presents an interactive gesture control system based on Leap Motion Controller that can overcome these shortcomings. A coordinate transformation is performed between the position of the left palm detected by Leap Motion Controller and the tool center point (TCP) of the collaborative manipulator to obtain gesture data first. This gesture data is used to control the posture of a six-joint collaborative manipulator-Lite6. The position of gripper is controlled by grip level of the right palm. A controller is designed to manipulate a lift table on which Leap Motion Controller is placed to achieve adaptive ascending and descending movements to meet the needs of operators of different heights and arm spans. A modified Kalman filter is used to filter noise and smooth the signal captured by Leap Motion Controller. The experiments demonstrate that the system can operate stably and accurately control the position, posture, and gripper position of a collaborative manipulator in real time using human palms’ gestures.

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“…Thus, there is a barrier in communication between deaf and normal people where some messages could not be delivered and interpreted correctly. The difficulties in this communication can be overcome by introducing automatic recognition of hand gestures using artificial intelligence [1][2][3][4][5][6].The detection of several gestures such as palm-clench, palm-open, hand-backward, hand-forward, pinch-part, pinch together, swipe right, swipe left, zoom in, zoom out, pushing, pulling, lifting a hand, and shaking hand have been…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network

Malik,

Mansor,

Abdul Rashid

et al. 2023

IJIE

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The difficulties in the communication between the deaf and normal people through sign language can be overcome by implementing deep learning in the gestures signal recognition. The use of the Convolution Neural Network (CNN) in distinguishing radar-based gesture signals of deaf sign language has not been investigated.This paper describes the recognition of gestures of deaf sign language using radar and CNN. Six gestures of deaf sign language were acquired from normal subjects using a radar system and processed. Short-time Fourier Transform wasperformed to extract the gestures features and the classification was performed using CNN. The performance of CNN was examined using two types of inputs; segmented and non-segmented spectrograms. The accuracy of recognising the gestures is higher (92.31%)using the non-segmented spectrograms compared to the segmented spectrogram. The radar-based deaf sign language could be recognised accurately using CNN without segmentation.

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Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

Cited by 15 publications

References 29 publications

Renal Cancer Detection: Fusing Deep and Texture Features from Histopathology Images

Renal Cancer Detection: Fusing Deep and Texture Features from Histopathology Images

An interactive gesture control system for collaborative manipulator based on Leap Motion Controller

Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network

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