scholarly journals Fire Detection Using Multi Criteria Image Processing Technique in Video Sequences

2015 ◽  
Vol 16 (1) ◽  
pp. 136
Author(s):  
Behrouz Memarzadeh ◽  
Mohammad Ali Mohammadi
Keyword(s):  
High Speed ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Processing Technique ◽  
Video Data ◽  
Image Processing Technique ◽  
Video Sequences ◽  
False Alarms ◽  
Surveillance Systems ◽  
Gray Level

Vision-based flame detection has drawn significant attention in the past decade with camera surveillance systems becoming ubiquitous. This paper proposes a multi criterion method to detect fire or flames by processing the video data generated by a high speed camera. Since flame images are special class of images, some of the unique features of a flame may be used to identify flame. There are some differences between flame images and other general images. By using these features we are able to detect fire correctly with least false alarm. In this paper we present an algorithm which can detect fire and reduce number of false alarms by counting number of identified pixels. In the algorithm, we preprocess the images to have better results. So first we adjust the gray level of a flame image according to its statistical distribution to have better processing. After that we try to extract fire features in images. First by using color characteristics, the ratio of red to green, we can identify probable fire-like or fire like pixels. Second, to highlight the regions with high gray level contrast at their edges, we use the extended prewitt filter. We use AND operation on two above processing images to remove unrelated pixels, at last by using flicker frequency, the oscillating change in the number of identified pixels over time is transformed into the frequency domain to complete detection algorithm. Simulation proves the algorithm ability to detect fire in different situations in video sequences.

Computational Estimation of Bubble Gas Velocity for Slug Flow Pattern in Horizontal Pipes Using Image Processing Technique

2012 ◽  
Author(s):  
María T. Valecillos ◽  
Carlos H. Romero ◽  
María A. Márquez ◽  
Sissi D. Vergara
Keyword(s):  
Image Processing ◽  
Flow Pattern ◽  
Slug Flow ◽  
High Speed ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Gas Velocity ◽  
Two Phase ◽  
Horizontal Pipes ◽  
Computational Estimation

Two-phase slug flow pattern is one of the most common flow patterns present in many industries, therefore its study becomes relevant. The aim of this work was to develop an automated computational program to determine the bubble gas velocity associated to gas-liquid two-phase slug flow by using video digital image processing technique. In order to obtain the images for the analysis, experiments were carried out using a pipe bench for air-water two-phase flow. The experimental facility is located in Simon Bolivar University, in Venezuela. The system has three pipes with different internal diameters and can be rotated around its axis and fixed at any inclination angle from horizontal to vertical flow. The tests were run in a horizontal pipeline of 0.03175m of internal pipe diameter and 8m long. For slug flow visualization a high speed camera Kodak Ektapro 4540mx imager was used. The camera was located in an x/D relation corresponding to 249 from the pipe inlet, ensuring the complete development of the flow. The camera allowed a maximum acquisition velocity of 4500 frames per second. The superficial velocity range was 0.16–1.79m/s and 0.16–1.26m/s for air and water, respectively. To summarize, 165 tests were performed and 1320000 images were analyzed with 20 flow rate combinations. The computational application was validated by comparing it with the velocities measured manually over selected images. Results obtained were compared to several correlations such as Bendiksen [1], Cook & Behnia [2] and Wang et al. [3].

Determination of Two-Phase Flow Void Fraction of R600a in a Horizontal Smooth Tube

2012 ◽  
Author(s):  
Özden Ağra ◽  
Hakan Demir ◽  
Ş. Özgür Atayılmaz ◽  
Ahmet Yurtseven ◽  
A. Selim Dalkılıç ◽  
...  
Keyword(s):  
Neural Network ◽  
Void Fraction ◽  
High Speed ◽  
Saturation Temperature ◽  
Horizontal Tube ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Two Phase ◽  
Void Fractions

In this paper, the void fraction of alternative refrigerant R600a flowing inside horizontal tube is determined by means of an experimental technique, well known correlations in the literature and a generalized neural network analysis. The horizontal tube is made from smooth glass tubing of 4 mm inner diameter. The test runs are done at average saturated condensing temperatures between 30 and 40 °C while the average qualities and the mass fluxes are between 0.45–0.91 and 68.5–138.1 kg m-2s-1 respectively. The flow regime determination inside the tube is performed by means of sight glasses placed at the inlet and outlet sections of the test section, used for in-tube condensation tests, virtually. An image processing technique, performed by means of a high speed camera, is used to determine the void fractions of stratified and annular condensing flow of R600a experimentally. The void fractions are determined using relevant measured data together with 11 different void fraction models and correlations reported in the open literature analytically. Artificial neural network (ANN) analysis is developed to determine the void fractions numerically. For this aim, mass flow rate, average vapor quality, saturation temperature, liquid and vapor densities, liquid and vapor dynamic viscosities and surface tension are selected as the input parameters, while the void fraction is selected as the output. Three-layer network is used for predicting the void fraction. The number of the neurons in the hidden layer was determined by a trial and error process evaluating the performance of the network and standard sensitivity analysis. The measured void fraction values are found to be in good agreement with those from ANN analysis and correlations in the literature. It is also seen that the trained network are more predictive on the determination of void fraction than most of the investigated correlations.

scholarly journals GPU Accelerated real-time Melanoma Detection

2018 ◽  
Vol 7 (3) ◽  
pp. 1208
Author(s):  
Ajai Sunny Joseph ◽  
Elizabeth Isaac
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Processing Technique ◽  
Video Data ◽  
Image Processing Technique ◽  
Processing Unit ◽  
Central Processing ◽  
Melanoma Detection ◽  
Real Time Analysis ◽  
Graphical Processing

Melanoma is recognized as one of the most dangerous type of skin cancer. A novel method to detect melanoma in real time with the help of Graphical Processing Unit (GPU) is proposed. Existing systems can process medical images and perform a diagnosis based on Image Processing technique and Artificial Intelligence. They are also able to perform video processing with the help of large hardware resources at the backend. This incurs significantly higher costs and space and are complex by both software and hardware. Graphical Processing Units have high processing capabilities compared to a Central Processing Unit of a system. Various approaches were used for implementing real time detection of Melanoma. The results and analysis based on various approaches and the best approach based on our study is discussed in this work. A performance analysis for the approaches on the basis of CPU and GPU environment is also discussed. The proposed system will perform real-time analysis of live medical video data and performs diagnosis. The system when implemented yielded an accuracy of 90.133% which is comparable to existing systems.  

scholarly journals A practical approach to tramway track condition monitoring: vertical track defects detection and identification using time-frequency processing technique

2018 ◽  
Vol 13 (s1) ◽  
pp. 135-146
Author(s):  
Péter Bocz ◽  
Ákos Vinkó ◽  
Zoltán Posgay
Keyword(s):  
Surface Defects ◽  
Detection Algorithm ◽  
Processing Technique ◽  
False Alarms ◽  
Distribution Analysis ◽  
Time Frequency ◽  
Detection And Identification ◽  
Track Condition ◽  
Automatic Detection Algorithm ◽  
Track Irregularities

Abstract This paper presents an automatic method for detecting vertical track irregularities on tramway operation using acceleration measurements on trams. For monitoring of tramway tracks, an unconventional measurement setup is developed, which records the data of 3-axes wireless accelerometers mounted on wheel discs. Accelerations are processed to obtain the vertical track irregularities to determine whether the track needs to be repaired. The automatic detection algorithm is based on time–frequency distribution analysis and determines the defect locations. Admissible limits (thresholds) are given for detecting moderate and severe defects using statistical analysis. The method was validated on frequented tram lines in Budapest and accurately detected severe defects with a hit rate of 100%, with no false alarms. The methodology is also sensitive to moderate and small rail surface defects at the low operational speed.

Research on Length Measurement of Workpiece Based on Digital Image Processing Technique

2013 ◽  
Vol 655-657 ◽  
pp. 859-867
Author(s):  
Bin Luo ◽  
Wei Liu ◽  
Yun Luo
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
High Speed ◽  
Vision System ◽  
Measuring Method ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Measuring Accuracy ◽  
Machine Speed

Aiming at the problems such as slow speed and low measuring accuracy existing in measuring length precision of pin parts in mass production, a new approach of high speed and precise measurement is proposed based on the research of the length measuring methods such as parallel laser length measuring method and area array CCD measuring method, etc. Application of (machine vision system and computer measurement & control technology) digital image processing technology can perform automatic high speed measurement and separation of workpiece effectively and accurately. The experimental results show that the machine speed could reach 4-5 pieces/second, with length measuring accuracy of around 10 microns, indicating that the approach proposed in this paper has important practical value.

scholarly journals An Effective Model to Alert a Drowsy Driver using Eye Closure Rate

2020 ◽  
Vol 9 (5) ◽  
pp. 2409-2412
Keyword(s):  
Network Architecture ◽  
High Speed ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Road Accidents ◽  
Prevention Measures ◽  
Drowsy Driving ◽  
Closure Rate ◽  
The Road ◽  
Eye Closure

Transportation plays a major role in today’s world. To move from one place to another place (long distances) which cannot be covered by walk, we use vehicles which consumes less time to reach destination. According to statistics, by 2050 the urban population will increase by 68% which leads to an increase in transportation that causes pollution and increase in the rate of road accidents. There are many methods and prevention measures to control pollution. The road accidents are caused due to distracted driving, high speed, drowsy driving and disobeying traffic rules. Among these, drowsy driving has been a cause for 20% of road accidents which is because of fatigue driving. In this article, a model is proposed based on image processing technique which is segmentation and a deep convolutional neural network architecture to improve the performance of the model when compared to the existing models. The proposed model works with better performance in different lighting conditions.

scholarly journals ROI-based efficient video data processing for large-scale cloud storage in intelligent CCTV environment

2018 ◽  
Vol 7 (3.3) ◽  
pp. 151 ◽  
Author(s):  
Dong Hyeok Lee ◽  
Nam Je. Park
Keyword(s):  
Big Data ◽  
Cloud Storage ◽  
Large Scale ◽  
Image Data ◽  
Processing Technique ◽  
Video Data ◽  
Image Processing Technique ◽  
Efficient Data ◽  
Transmission Structure ◽  
Cloud Storage Environment

Background/Objectives: Big data environment is being realized. Recently, intelligent public safety environment on the foundation of the image processing technique based on big data is being introduced, and accordingly, processing CCTV images is becoming more important day by day.Methods/Statistical analysis: In this paper, an efficient technique to send image information for mass cloud storage environment was proposed. With the offered method, only the ROI area is extracted and partial object images are transmitted, and it has the strengths of higher efficiency and protected privacy with the application of a masking technique.Findings: it is general to apply the masking technique partially to face information, and in this study, the privacy of the image data registered in the cloud storage was to be protected based on this masking technique, and an efficient data transmission structure grounded on ROI area extraction was proposed.Improvements/Applications: With the offered method, only the ROI area is extracted and partial object images are transmitted, and it has the strengths of higher efficiency and protected privacy with the application of a masking technique.  

scholarly journals Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset

2021 ◽  
Vol 11 (16) ◽  
pp. 7754
Author(s):  
Sung-Woon Jung ◽  
Hyuk-Ju Kwon ◽  
Sung-Hak Lee
Keyword(s):  
Dynamic Range ◽  
Training Model ◽  
High Dynamic Range ◽  
Local Area ◽  
Processing Technique ◽  
Training Image ◽  
Image Processing Technique ◽  
Generative Adversarial Networks ◽  
Surveillance Systems ◽  
Proposed Model

High-dynamic-range (HDR) imaging is a digital image processing technique that enhances an image’s visibility by modifying its color and contrast ranges. Generative adversarial networks (GANs) have proven to be potent deep learning models for HDR imaging. However, obtaining a sufficient volume of training image pairs is difficult. This problem has been solved using CycleGAN, but the result of the use of CycleGAN for converting a low-dynamic-range (LDR) image to an HDR image exhibits problematic color distortion, and the intensity of the output image only slightly changes. Therefore, we propose a GAN training optimization model for converting LDR images into HDR images. First, a gamma shift method is proposed for training the GAN model with an extended luminance range. Next, a weighted loss map trains the GAN model for tone compression in the local area of images. Then, a regional fusion training model is used to balance the training method with the regional weight map and the restoring speed of local tone training. Finally, because the generated module tends to show a good performance in bright images, mean gamma tuning is used to evaluate image luminance channels, which are then fed into modules. Tests are conducted on foggy, dark surrounding, bright surrounding, and high-contrast images. The proposed model outperforms conventional models in a comparison test. The proposed model complements the performance of an object detection model even in a real night environment. The model can be used in commercial closed-circuit television surveillance systems and the security industry.

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