An Improved Hand Gesture Recognition with Two-Stage Convolution Neural Networks Using a Hand Color Image and its Pseudo-Depth Image

Liu, Jiaqing; Furusawa, Kotaro; Tateyama, Tomoko; Iwamoto, Yutaro; Chen, Yen‐Wei

doi:10.1109/icip.2019.8802970

Cited by 20 publications

(2 citation statements)

References 9 publications

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“…Stergiopoulou proposed another self-growing and self-organized neural gas (SONG) network [18] for hand motion acknowledgment. For hand district discovery, a shading division procedure dependent on the skin shading channel in the YCbCr shading space [19] was utilized, and an estimation of hand-formed morphology has been recognized utilizing SONG organize; three highlights were extricated, utilizing the finger-distinguishing proof procedure that decides the quantity of the lifted fingers and quality of shape of the hand, and the Gaussian circulation model was utilized for acknowledgment. Table 1 shows a review of the contributions by the researchers in a similar domain as the proposed work.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision-Enabled Character Recognition of Hand Gestures for Patients with Hearing and Speaking Disability

Juneja¹,

Juneja²,

Dhiman³

et al. 2021

Mobile Information Systems

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Hand gesture recognition is one of the most sought technologies in the field of machine learning and computer vision. There has been an unprecedented demand for applications through which one can detect the hand signs for deaf people and people who use sign language to communicate, thereby detecting hand signs and correspondingly predicting the next word or recommending the word that may be most appropriate, followed by producing the word that the deaf people and people who use sign language to communicate want to say. This article presents an approach to develop such a system by that we can determine the most appropriate character from the sign that is being shown by the user or the person to the system. To enable pattern recognition, various machine learning techniques have been explored and we have used the CNN networks as a reliable solution in our context. The creation of such a system involves several convolution layers through which features have been captured layer by layer. The gathered features from the image are further used for training the model. The trained model efficiently predicts the most appropriate character in response to the sign exposed to the model. Thereafter, the predicted character is used to predict further words from it according to the recommendation system used in this case. The proposed system attains a prediction accuracy of 91.07%.

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Section: Literature Reviewmentioning

confidence: 99%

Computer Vision-Enabled Character Recognition of Hand Gestures for Patients with Hearing and Speaking Disability

Juneja¹,

Juneja²,

Dhiman³

et al. 2021

Mobile Information Systems

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“…Reinforcement learning is another method used to solve the space exploration problem of mobile robots in unknown environments [ 10 , 11 ]. This is due to its characteristics that enable agents to have learning capabilities and recognition, e.g., through image processing.…”

Section: Introductionmentioning

confidence: 99%

A Soar-Based Space Exploration Algorithm for Mobile Robots

Luo

Zhou

Fuentes

et al. 2022

Entropy

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Space exploration is a hot topic in the application field of mobile robots. Proposed solutions have included the frontier exploration algorithm, heuristic algorithms, and deep reinforcement learning. However, these methods cannot solve space exploration in time in a dynamic environment. This paper models the space exploration problem of mobile robots based on the decision-making process of the cognitive architecture of Soar, and three space exploration heuristic algorithms (HAs) are further proposed based on the model to improve the exploration speed of the robot. Experiments are carried out based on the Easter environment, and the results show that HAs have improved the exploration speed of the Easter robot at least 2.04 times of the original algorithm in Easter, verifying the effectiveness of the proposed robot space exploration strategy and the corresponding HAs.

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