On the recognition of human hand touch from robotic skin pressure measurements using convolutional neural networks

2017 ◽  
Author(s):  
Alessandro Albini ◽  
Simone Denei ◽  
Giorgio Cannata
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Human Hand ◽  
Pressure Measurements ◽  
Skin Pressure

scholarly journals Convolutional neural networks framework for human hand gesture recognition

2021 ◽  
Vol 10 (4) ◽  
pp. 2223-2230
Author(s):  
Aseel Ghazi Mahmoud ◽  
Ahmed Mudheher Hasan ◽  
Nadia Moqbel Hassan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Human Hand ◽  
Language Recognition ◽  
Hand Gestures ◽  
Recognition Systems

Recently, the recognition of human hand gestures is becoming a valuable technology for various applications like sign language recognition, virtual games and robotics control, video surveillance, and home automation. Owing to the recent development of deep learning and its excellent performance, deep learning-based hand gesture recognition systems can provide promising results. However, accurate recognition of hand gestures remains a substantial challenge that faces most of the recently existing recognition systems. In this paper, convolutional neural networks (CNN) framework with multiple layers for accurate, effective, and less complex human hand gesture recognition has been proposed. Since the images of the infrared hand gestures can provide accurate gesture information through the low illumination environment, the proposed system is tested and evaluated on a database of hand-based near-infrared which including ten gesture poses. Extensive experiments prove that the proposed system provides excellent results of accuracy, precision, sensitivity (recall), and F1-score. Furthermore, a comparison with recently existing systems is reported.

scholarly journals Hand Side Recognition and Authentication System based on Deep Convolutional Neural Networks

2021 ◽  
Vol 10 (4) ◽  
pp. 5-13
Author(s):  
Mohammad Abbadi ◽  
Afaf Tareef ◽  
Afnan Sarayreh
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Human Hand ◽  
Biometric Authentication ◽  
Deep Convolutional Neural Networks ◽  
Average Accuracy ◽  
Morphological Filtering ◽  
Contour Deformation ◽  
Authentication System ◽  
Hand Image

The human hand has been considered a promising component for biometric-based identification and authentication systems for many decades. In this paper, hand side recognition framework is proposed based on deep learning and biometric authentication using the hashing method. The proposed approach performs in three phases: (a) hand image segmentation and enhancement by morphological filtering, automatic thresholding, and active contour deformation, (b) hand side recognition based on deep Convolutional Neural Networks (CNN), and (c) biometric authentication based on the hashing method. The proposed framework is evaluated using a very large hand dataset, which consists of 11076 hand images, including left/ right and dorsal/ palm hand images for 190 persons. Finally, the experimental results show the efficiency of the proposed framework in both dorsal-palm and left-right recognition with an average accuracy of 96.24 and 98.26, respectively, using a completely automated computer program.

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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