scholarly journals Image Processing Techniques-based fire detection

2021 ◽  
Vol 8 (1) ◽  
pp. 23-34
Author(s):  
Awa Ahmed ◽  
◽  
Osman Sharif ◽  
◽  
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Fire Detection ◽  
Timely Manner ◽  
Loss Of Life ◽  
Detection Algorithms ◽  
Detection Systems ◽  
Huge Impact ◽  
Different Color ◽  
Processing Techniques

In this paper different fire detection systems and techniques has been reviewed, many techniques have been developed for the purpose of early fire detection in different scenarios. The most accurate technique used among all these methods is Image Processing based Techniques. Different color models like RGB, HSI, CIE L*a*b and YCbCr have been used along with different edge detection algorithms like Sobel and Novel edge detection, finally the color segmentation technique was discussed in the review paper. All the mentioned methods in these papers have significantly proved to detect fire and flame edges in digital images with a timely manner, which has a huge impact on saving life and reducing loss of life.

Cellular Automata Algorithms for Digital Image Processing

Biometrics ◽  
2017 ◽  
pp. 382-402
Author(s):  
Petre Anghelescu
Keyword(s):  
Image Processing ◽  
Cellular Automata ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Understanding ◽  
Software Application ◽  
Detection Algorithms ◽  
Human Image ◽  
The Impact

In this paper are presented solutions to develop algorithms for digital image processing focusing particularly on edge detection. Edge detection is one of the most important phases used in computer vision and image processing applications and also in human image understanding. In this chapter, implementation of classical edge detection algorithms it is presented and also implementation of algorithms based on the theory of Cellular Automata (CA). This work is totally related to the idea of understanding the impact of the inherently local information processing of CA on their ability to perform a managed computation at the global level. If a suitable encoding of a digital image is used, in some cases, it is possible to achieve better results in comparison with the solutions obtained by means of conventional approaches. The software application which is able to process images in order to detect edges using both conventional algorithms and CA based ones is written in C# programming language and experimental results are presented for images with different sizes and backgrounds.

Identity Concealment When Uploading Pictures of Patients in a Tele-Medicine System

2019 ◽  
Vol 10 (2) ◽  
pp. 44-62
Author(s):  
Judith Jumig Azcarraga ◽  
John Zachary Raduban ◽  
Ma. Christine Gendrano ◽  
Arnulfo P. Azcarraga
Keyword(s):  
Image Processing ◽  
Patient Information ◽  
Medical Records ◽  
Exact Distribution ◽  
Eye Detection ◽  
Unauthorized Access ◽  
Image Processing Techniques ◽  
Detection Algorithms ◽  
Identity Concealment ◽  
Processing Techniques

Tele-medicine systems run the risk of unauthorized access to medical records, and there is greater possibility for the unlawful sharing of sensitive patient information, including children, and possibly showing their private parts. Aside from violating their right to privacy, such practices discourage patients from subjecting themselves to tele-medicine. The authors thus present an automatic identity concealment system for pictures, the way it is designed in the GetBetter tele-medicine system developed under a WHO/TDR grant. Based on open-source face- and eye-detection algorithms, identity concealment is executed by blurring the eye region of a detected face using pixel shuffling. This method is shown to be not only effective in concealing the identity of the patient, but also in preserving the exact distribution of pixel values in the image. This is useful when subsequent image processing techniques are employed, such as when identifying the type of lesions based on images of the skin.

scholarly journals Hardware Implementation of Sobel Edge Detection Algorithm

2020 ◽  
Vol 32 ◽  
pp. 03051
Author(s):  
Ankita Pujare ◽  
Priyanka Sawant ◽  
Hema Sharma ◽  
Khushboo Pichhode
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Software Tool ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Sobel Edge Detection ◽  
Image Edges

In the fields of image processing, feature detection, the edge detection is an important aspect. For detection of sharp changes in the properties of an image, edges are recognized as important factors which provides more information or data regarding the analysis of an image. In this work coding of various edge detection algorithms such as Sobel, Canny, etc. have been done on the MATLAB software, also this work is implemented on the FPGA Nexys 4 DDR board. The results are then displayed on a VGA screen. The implementation of this work using Verilog language of FPGA has been executed on Vivado 18.2 software tool.

A Forest Fire Detection System

2013 ◽  
pp. 179-190
Author(s):  
Vassileios Tsetsos ◽  
Odysseas Sekkas ◽  
Evagellos Zervas
Keyword(s):  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Decision Fusion ◽  
Visible Range ◽  
Detection Algorithms ◽  
Integrated Technology ◽  
Detection Systems ◽  
Individual Decisions ◽  
Fusion Scheme

Forest fires cause immeasurable damages to indispensable resources for human survival, destroy the balance of earth ecology, and worst of all they frequently cost human lives. In recent years, early fire detection systems have emerged to provide monitoring and prevention of the disasterous forest fires. Among them, the Meleager1 system aims to offer one of the most advanced and integrated technology solutions for fire protection worldwide by integrating several innovative features. This chapter outlines one of the major components of the Meleager system, that is the visual fire detection sybsystem. Groundbased visible range PTZ cameras monitor the area of interest, and a low level decision fusion scheme is used to combine individual decisions of numerous fire detection algorithms. Personalized alerts and induced feedback is used to adapt the detection process and improve the overall system performance.

Hypertensive Retinopathy Classification Using Improved Clustering Algorithm and the Improved Convolution Neural Network

2022 ◽  
pp. 119-131
Author(s):  
Bhimavarapu Usharani
Keyword(s):  
Image Processing ◽  
Clustering Algorithm ◽  
Activation Function ◽  
Current Approach ◽  
Hypertensive Retinopathy ◽  
Fundus Images ◽  
Image Processing Techniques ◽  
Detection Systems ◽  
Processing Techniques ◽  
Fuzzy Image

Hypertensive retinopathy is a disorder that causes hypertension which includes abnormalities in the retina that triggers vision problems. An effective automatic diagnosis and grading of the hypertensive retinopathy would be very useful in the health system. This chapter presents an improved activation function on the CNN by recognizing the lesions present in the retina and afterward surveying the influenced retina as indicated by the hypertensive retinopathy various sorts. The current approach identifies the symptoms associated of retinopathy for hypertension. This chapter presents an up-to-date review on hypertensive retinopathy detection systems that implement a variety of image processing techniques, including fuzzy image processing, along various improved activation function techniques used for feature extraction and classification. The chapter also highlights the available public databases, containing eye fundus images, which can be currently used in the hypertensive retinopathy research.

Image Processing for the Analysis of an Evolving Broken-Ice Field in Model Testing

2012 ◽  
Author(s):  
Qin Zhang ◽  
Solange van der Werff ◽  
Ivan Metrikin ◽  
Sveinung Løset ◽  
Roger Skjetne
Keyword(s):  
Image Processing ◽  
Water Area ◽  
Test Results ◽  
Detection Algorithms ◽  
Ice Concentration ◽  
Broken Ice ◽  
Ice Field ◽  
Ice Floes ◽  
Processing Techniques ◽  
Ice Forces

Dynamic positioning (DP) experiments in model ice were carried out in the ice tank at the Hamburg Ship Model Basin (HSVA) in the summer of 2011. In these experiments the behavior of two different ships in a broken-ice field were studied. One of the main parameters characterizing a broken-ice field is the ice concentration, defined as the fraction of the total water area covered by ice. In this paper, image processing techniques are applied to derive the ice concentration in the model basin. Several points in time are analyzed in order to describe the evolution of the ice field. The applied techniques include methods for identifying individual ice floes and calculating the ice concentration in the vicinity of the model ship. Ice floe boundaries are then obtained, and the ice floe size distribution and shape factor may further be extracted from the images. The image processing methods applied in this work are object extraction and edge detection algorithms, which are further customized to ice identification. The obtained results can be used for relating the ice field characteristics to the model test results, such as the vessel’s displacements and the corresponding ice forces.

A Novel Approach for Edge Detection in Images Based on Cellular Learning Automata

2012 ◽  
Vol 2 (4) ◽  
pp. 51-61 ◽  
Author(s):  
Farhad Soleimanian Gharehchopogh ◽  
Samira Ebrahimi
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Learning Automata ◽  
Edge Detector ◽  
Noise Elimination ◽  
Minimum Period ◽  
Detection Algorithms ◽  
Novel Approach ◽  
Result Show ◽  
Cellular Learning Automata

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image processing. Many studios here been conducted till now in edge detection algorithms of various conditions. In this study a new method for edge detection in images with the use of CLA is recommended. The proposed method of edge detection in images was tested with different sizes and the results were compared with Sobel edge detector classic method. The result show that the method based on CLA has a desirable performance in edge detection and compares the images with a more uniformity during a minimum period of time.

scholarly journals Visual Sensor Based Image Segmentation by Fuzzy Classification and Subregion Merge

2017 ◽  
Vol 2017 ◽  
pp. 1-15
Author(s):  
Huidong He ◽  
Xiaoqian Mao ◽  
Wei Li ◽  
Linwei Niu ◽  
Genshe Chen
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Color Image ◽  
Color Image Segmentation ◽  
Fuzzy Classification ◽  
Spatial Proximity ◽  
Visual Sensor ◽  
Detection Algorithms ◽  
Color Similarity ◽  
Different Color

The extraction and tracking of targets in an image shot by visual sensors have been studied extensively. The technology of image segmentation plays an important role in such tracking systems. This paper presents a new approach to color image segmentation based on fuzzy color extractor (FCE). Different from many existing methods, the proposed approach provides a new classification of pixels in a source color image which usually classifies an individual pixel into several subimages by fuzzy sets. This approach shows two unique features: the spatial proximity and color similarity, and it mainly consists of two algorithms: CreateSubImage and MergeSubImage. We apply the FCE to segment colors of the test images from the database at UC Berkeley in the RGB, HSV, and YUV, the three different color spaces. The comparative studies show that the FCE applied in the RGB space is superior to the HSV and YUV spaces. Finally, we compare the segmentation effect with Canny edge detection and Log edge detection algorithms. The results show that the FCE-based approach performs best in the color image segmentation.

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