scholarly journals THE COMPONENT METHOD OF SCENE ANALYSIS AND OBJECT RECOGNITION

2014 ◽  
pp. 47-51
Author(s):  
Aleksander Vechur ◽  
Aleksey Chayka ◽  
Yuriy Chemodakov
Keyword(s):  
Object Recognition ◽  
Theoretical Analysis ◽  
Real Time ◽  
Experimental Results ◽  
Natural Images ◽  
Scene Analysis ◽  
Small Computer ◽  
Multiple Features ◽  
Component Method ◽  
Computer Resources

The main aim of this work is to propose method that allows some machine to determine surrounding objects. It is important that algorithm must use small computer resources so that machine could work in real time. To improve segmentation on natural images, it is necessary to combine multiple features effectively. Our experimental results are consistent with the theoretical analysis.

PHOTOINDUCED NONLINEAR OCTUPOLAR POLARIZATION: TRANSIENT AND PERMANENT REGIMES

1996 ◽  
Vol 05 (04) ◽  
pp. 653-670 ◽  
Author(s):  
CÉLINE FIORINI ◽  
JEAN-MICHEL NUNZI ◽  
FABRICE CHARRA ◽  
IFOR D.W. SAMUEL ◽  
JOSEPH ZYSS
Keyword(s):  
Theoretical Analysis ◽  
Second Harmonic ◽  
Experimental Results ◽  
Polar Field ◽  
Rotational Spectrum ◽  
Dual Frequency ◽  
One Dimensional ◽  
Ethyl Violet ◽  
Disperse Red 1 ◽  
Good Agreement

An original poling method using purely optical means and based on a dual-frequency interference process is presented. We show that the coherent superposition of two beams at fundamental and second-harmonic frequencies results in a polar field with an irreducible rotational spectrum containing both a vector and an octupolar component. This enables the method to be applied even to molecules without a permanent dipole such as octupolar molecules. After a theoretical analysis of the process, we describe different experiments aiming at light-induced noncentrosymmetry performed respectively on one-dimensional Disperse Red 1 and octupolar Ethyl Violet molecules. Macroscopic octupolar patterning of the induced order is demonstrated in both transient and permanent regimes. Experimental results show good agreement with theory.

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 OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Real-Time Identification and Tracking of Infrared Markers Based on Kalman Filter

2012 ◽  
Vol 249-250 ◽  
pp. 1147-1153
Author(s):  
Qiao Na Xing ◽  
Da Yuan Yan ◽  
Xiao Ming Hu ◽  
Jun Qin Lin ◽  
Bo Yang
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
Real Time ◽  
Complex Terrain ◽  
Experimental Results ◽  
Identification Method ◽  
In The Wild ◽  
Real Time Identification

Automatic equipmenttransportation in the wild complex terrain circumstances is very important in rescue or military. In this paper, an accompanying system based on the identification and tracking of infrared LEDmarkers is proposed. This system avoidsthe defect that visible-light identification method has. In addition, this paper presents a Kalman filter to predict where infraredmarkers may appear in the nextframe imageto reduce the searchingarea of infrared markers, which remarkablyimproves the identificationspeed of infrared markers. The experimental results show that the algorithm proposed in this paper is effective and feasible.

Implementation of a High-Precision Ultrasonic Rain Gauge

2013 ◽  
Vol 300-301 ◽  
pp. 382-388
Author(s):  
Zhan Wei Xu ◽  
Gui Lin Zheng
Keyword(s):  
Theoretical Analysis ◽  
High Precision ◽  
Rain Gauge ◽  
Experimental Results ◽  
Full Description ◽  
Self Calibration ◽  
Velocity Of Ultrasound

A novel rain gauge based on acoustic self-calibration principle is proposed in the paper. Acoustic self-calibration principle can eliminate the uncertainty of the velocity of ultrasound and achieve accurate measurement of rainfall. The rain gauge not only overcomes the influence on the rainfall measurement under intensive rainfall conditions, but also improves the precision of rain gauge. Plenty of experiments have been done to validate the design. Both theoretical analysis and experimental results show the effectiveness of the rain gauge. A full description of the rain gauge and implementation are presented.

scholarly journals High-Accuracy Tracking Using Ultrawideband Signals for Enhanced Safety of Cyclists

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Davide Dardari ◽  
Nicoló Decarli ◽  
Anna Guerra ◽  
Ashraf Al-Rimawi ◽  
Víctor Marín Puchades ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
High Accuracy ◽  
Experimental Results ◽  
Localization Error ◽  
Localization System ◽  
Dynamic Conditions ◽  
The Road ◽  
Road Users

In this paper, an ultrawideband localization system to improve the cyclists’ safety is presented. The architectural solutions proposed consist of tags placed on bikes, whose positions have to be estimated, and anchors, acting as reference nodes, located at intersections and/or on vehicles. The peculiarities of the localization system in terms of accuracy and cost enable its adoption with enhanced risk assessment units situated on the infrastructure/vehicle, depending on the architecture chosen, as well as real-time warning to the road users. Experimental results reveal that the localization error, in both static and dynamic conditions, is below 50 cm in most of the cases.

GPU processing for parallel image processing and real-time object recognition

2014 ◽  
Author(s):  
Kevin Vincent ◽  
Damien Nguyen ◽  
Brian Walker ◽  
Thomas Lu ◽  
Tien-Hsin Chao
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Real Time ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
Gpu Processing
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