Forest Fire Smoke Detection in Video Based on Digital Image Processing Approach with Static and Dynamic Characteristic Analysis

2011 ◽  
Author(s):  
Surapong Surit ◽  
Watchara Chatwiriya
Keyword(s):  
Image Processing ◽  
Dynamic Characteristic ◽  
Digital Image Processing ◽  
Forest Fire ◽  
Digital Image ◽  
Smoke Detection ◽  
Characteristic Analysis ◽  
Processing Approach ◽  
Fire Smoke

scholarly journals AN OPTIMIZED SURF-BASED FIRE AND SMOKE DETECTION SYSTEM USING IMAGE PROCESSING FROM SURVEILLANCE VIDEO.

2020 ◽  
Vol 9 (10) ◽  
pp. 86-96
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Optimization Approach ◽  
Surveillance Video ◽  
Smoke Detection ◽  
Video Frames ◽  
Optimal Feature

Over the last decades, digital image processing based fire and smoke detection have been improving steadily to provide a more accurate detection results in the area of surveillance security system. Detection of the fire and smoke from the surveillance videos is very challenging task due to the complex structural properties of the video frames or images and need improvisation in the existing work by utilization of feature selection or optimization approach to select on optimal feature according to the fire and smoke. A research based on the combination of various feature extraction techniques with feature selection approach for fire and smoke detection has been presented in this paper. In this research, we develop Fire and Smoke Detection (FSD) system using digital image processing with the concept of Speed up Robust Feature (SURF) along with the Intelligent Water Drops (IWD) as a feature selection and optimization algorithm. Here, Artificial Neural Network (ANN) is used as an Artificial Intelligence (AI) technique with that helps to select a set of optimal feature from the extracted by SURF descriptor from the video frames. By utilizing the concept of optimized ANN, the accuracy of proposed FSD system is increases in terms of detection accuracy and with minimum percentage of error. At last, the performance of the FSD system is calculated to validate the model and this shows that it is possible to use IWD with SURF as a feature extraction technique in order to detect the fire or smoke form the surveillance video with minimum error rate and the simulation results clearly show the effectiveness of proposed FSD system

scholarly journals Mobile Screening Framework of Anterior Segment Photographed Images

2018 ◽  
Vol 7 (4.11) ◽  
pp. 85
Author(s):  
N Syahira M Zamani ◽  
Laily Azyan Ramlan ◽  
W Mimi Diyana W Zaki ◽  
Aini Hussain ◽  
Haliza Abdul Mutalib
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Anterior Segment ◽  
Qualitative Assessment ◽  
Computational Time ◽  
Future Studies ◽  
Processing Approach ◽  
Processing Platform ◽  
Mobile Screening

This work presents a qualitative measurement of anterior segment photographed images (ASPIs) to identify between normal eyes and eyes with pterygium and pinguecula through Otsu multi-thresholding approach without contrast enhancement. In addition, we also propose a mobile screening framework of ASPIs through smartphones. ASPIs were directly sent to the cloud storage once an ASPI was captured using a smartphone camera, and then each image was processed through a digital image processing approach in a processing platform. Three important steps, namely, pre-processing, image segmentation and qualitative assessment, are involved in the processing platform of the mobile screening framework. The ASPIs are pre-processed to minimise or eliminate any unwanted areas within the image. Then, these ASPIs are segmented through multi-thresholding Otsu approach with clustering number n = 3. Segmentation result shows that the accuracy of the proposed method is 87.5%, which is comparable with the previously established work that has applied three-step differencing (3SD) method. However, the proposed approach has better computational time which is six times faster than the 3SD method. These results demonstrate a remarkable effort to produce a simple but straightforward digital image processing approach to be implemented in cloud computing for future studies.                                                                                       

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