Real-time color classification of objects from video streams

2017 ◽  
Author(s):  
G Pavithra ◽  
J Jency Jose ◽  
T A Chandrappa
Keyword(s):  
Real Time ◽  
Video Streams ◽  
Color Classification

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

An Application and Research of Gray-Relation for Color Classification in Dyeing Textile

2013 ◽  
Vol 694-697 ◽  
pp. 2881-2885
Author(s):  
Hai Yan Wang ◽  
Jian Xin Zhang
Keyword(s):  
System Analysis ◽  
High Efficiency ◽  
Information Management System ◽  
Space Distribution ◽  
Color Classification ◽  
Uniform Sampling ◽  
Forecast Precision ◽  
Internal Instability ◽  
The Stability

Dyeing textile’s information management system is the basis of accurate classification of color， machine studying methods have became a popular area of research for application in color classification. Traditional classification methods have high efficiency and are very simple ， but they are dependent on the distribution of sample spaces. If the sample data properties are not independent， forecast precision will been affected badly and internal instability will appear. An application of Gray-Relation for dyeing textile color classification has been designed， which offsets the discount in mathematical statistics method for system analysis. It is applicable regardless of variant in sample size， while quantizing structure is in agreement with qualitative analysis. On the basis of theoretical analysis， Dyeing textile color classification was conducted in the conditions of random sampling、 uniform sampling and stratified sampling. The experimental results proofs that by using Gray-Relation， dyeing textile color classification does not need to be dependent on sample space distribution， and increases the stability of classification.

Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B

2017 ◽  
Vol 89 ◽  
pp. 18-23 ◽  
Author(s):  
Yang Chen ◽  
Yan Luo ◽  
Wei Huang ◽  
Die Hu ◽  
Rong-qin Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Real Time ◽  
Chronic Hepatitis B ◽  
Hepatic Fibrosis ◽  
Tissue Elastography

A Scheduling Method for Real-time Decoding of MPEG-4 Video Streams

2005 ◽  
Author(s):  
Jinhwan Kim ◽  
Inhwan Jung ◽  
Yong Song ◽  
Bong Lee
Keyword(s):  
Real Time ◽  
Video Streams ◽  
Scheduling Method

Real-time classification of wooden boards

1991 ◽  
Author(s):  
Wolfgang Poelzleitner ◽  
Gert Schwingskakl
Keyword(s):  
Real Time ◽  
Real Time Classification

scholarly journals On a possible classification of real-time constructed sequences

1989 ◽  
Vol 65 (2) ◽  
pp. 143-148
Author(s):  
Noelle Bleuzen-Guernalec
Keyword(s):  
Real Time

scholarly journals Real-time classification of hand movements as a basis for intuitive control of grasp neuroprostheses

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Dmitry Amelin ◽  
Ivan Potapov ◽  
Josep Cardona Audí ◽  
Andreas Kogut ◽  
Rüdiger Rupp ◽  
...  
Keyword(s):  
Neural Networks ◽  
Standard Deviation ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Healthy Subjects ◽  
Hand Movements ◽  
Cord Injury ◽  
Field Programmable

AbstractThis paper reports on the evaluation of recurrent and convolutional neural networks as real-time grasp phase classifiers for future control of neuroprostheses for people with high spinal cord injury. A field-programmable gate array has been chosen as an implementation platform due to its form factor and ability to perform parallel computations, which are specific for the selected neural networks. Three different phases of two grasp patterns and the additional open hand pattern were predicted by means of surface Electromyography (EMG) signals (i.e. Seven classes in total). Across seven healthy subjects, CNN (Convolutional Neural Networks) and RNN (Recurrent Neural Networks) had a mean accuracy of 85.23% with a standard deviation of 4.77% and 112 µs per prediction and 83.30% with a standard deviation of 4.36% and 40 µs per prediction, respectively.

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