scholarly journals Computer Vision and Human-Machine Interaction in Industrial and Factory Automation [breaker page]

2019 ◽  
Keyword(s):  
Computer Vision ◽  
Human Machine Interaction ◽  
Factory Automation ◽  
Machine Interaction

scholarly journals Speech Emotion Recognition Using MLP Classifier

2021 ◽  
pp. 218-222
Author(s):  
Nagaraja N Poojary ◽  
Dr. Shivakumar G S ◽  
Akshath Kumar B.H
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Social Interaction ◽  
Emotion Recognition ◽  
Basic Medium ◽  
Speech Emotion Recognition ◽  
Speech Sample ◽  
Human Machine Interaction ◽  
Mlp Classifier ◽  
Machine Interaction

Language is human's most important communication and speech is basic medium of communication. Emotion plays a crucial role in social interaction. Recognizing the emotion in a speech is important as well as challenging because here we are dealing with human machine interaction. Emotion varies from person to person were same person have different emotions all together has different way express it. When a person express his emotion each will be having different energy, pitch and tone variation are grouped together considering upon different subject. Therefore the speech emotion recognition is a future goal of computer vision. The aim of our project is to develop the smart emotion recognition speech based on the convolutional neural network. Which uses different modules for emotion recognition and the classifier are used to differentiate emotion such as happy sad angry surprise. The machine will convert the human speech signals into waveform and process its routine at last it will display the emotion. The data is speech sample and the characteristics are extracted from the speech sample using librosa package. We are using RAVDESS dataset which are used as an experimental dataset. This study shows that for our dataset all classifiers achieve an accuracy of 68%.

Hybrid Embroidery: Exploring Interactive Fabrication in Handcrafts

Leonardo ◽  
2020 ◽  
Vol 53 (4) ◽  
pp. 429-433
Author(s):  
Yi-Chin Lee ◽  
Daniel Cardoso Llach
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Computational Methods ◽  
Design Methods ◽  
Direct Manipulation ◽  
Generative Design ◽  
Human Machine Interaction ◽  
Machine Interaction

This paper presents Hybrid Embroidery, a framework for interactive fabrication that leverages computational methods to broaden the possibilities of the craft of embroidery. Combining embroidery techniques, generative design methods, computer vision and a computerized embroidery machine, we show how this framework elicits a variety of innovative fabrication experiences that emphasize open-ended exploration, improvisation and play. The paper documents this framework, a series of sample results, challenges and next steps. It further outlines some of its implications for supporting creative exploration through real-time and direct manipulation of materials and close human-machine interaction.

scholarly journals Particle Filter Tracking without Dynamics

2007 ◽  
Vol 4 (4) ◽  
pp. 169-177 ◽  
Author(s):  
Jaime Ortegon-Aguilar ◽  
Eduardo Bayro-Corrochano
Keyword(s):  
Computer Vision ◽  
Particle Filter ◽  
Background Subtraction ◽  
Particle Filters ◽  
Frame Rate ◽  
People Tracking ◽  
Human Machine Interaction ◽  
Controlled Environments ◽  
Low Frame Rate ◽  
Machine Interaction

People tracking is an interesting topic in computer vision. It has applications in industrial areas such as surveillance or human-machine interaction. Particle Filters is a common algorithm for people tracking; challenging situations occur when the target's motion is poorly modelled or with unexpected motions. In this paper, an alternative to address people tracking is presented. The proposed algorithm is based in particle filters, but instead of using a dynamical model, it uses background subtraction to predict future locations of particles. The algorithm is able to track people in omnidirectional sequences with a low frame rate (one or two frames per second). Our approach can tackle unexpected discontinuities and changes in the direction of the motion. The main goal of the paper is to track people from laboratories, but it has applications in surveillance, mainly in controlled environments.

Computer Vision for Human–Machine Interaction

2018 ◽  
pp. 127-145 ◽  
Author(s):  
Qiuhong Ke ◽  
Jun Liu ◽  
Mohammed Bennamoun ◽  
Senjian An ◽  
Ferdous Sohel ◽  
...  
Keyword(s):  
Computer Vision ◽  
Human Machine Interaction ◽  
Machine Interaction

scholarly journals COMPUTER VISION FOR HUMAN-MACHINE INTERACTION-REVIEW

2019 ◽  
Vol 2019 (02) ◽  
pp. 131-139 ◽  
Author(s):  
Dr. Suma V.
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Human Vision ◽  
Human Machine Interaction ◽  
The Neural Networks ◽  
Security Surveillance ◽  
Heath Care ◽  
Machine Interaction

The paper is a review on the computer vision that is helpful in the interaction between the human and the machines. The computer vision that is termed as the subfield of the artificial intelligence and the machine learning is capable of training the computer to visualize, interpret and respond back to the visual world in a similar way as the human vision does. Nowadays the computer vision has found its application in broader areas such as the heath care, safety security, surveillance etc. due to the progress, developments and latest innovations in the artificial intelligence, deep learning and neural networks. The paper presents the enhanced capabilities of the computer vision experienced in various applications related to the interactions between the human and machines involving the artificial intelligence, deep learning and the neural networks.

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