vj 1st review.pptx
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The document discusses a student project to develop a forest fire detection system using image processing. The proposed system would use a digital camera to capture images of forests and then convert the images from RGB to YCbCr color space to help identify fire pixels. Convolutional neural networks would be used to analyze the images and detect any forest fires. The goal is to help prevent wildfires and protect forests, wildlife and resources. A literature review presented related work on using color models and image processing techniques for forest fire detection. The project aims to minimize pollution, save forests, and prevent wild animal extinction.
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2nd review.pptx
1) Forest fires are often caused by heat generated in litter that ignites accidentally from human carelessness in summer. Current detection methods rely on air, temperature, and particle sensors that must be triggered by particles entering the sensors.
2) The proposed method uses image processing techniques to detect fires from images captured by a digital camera. It converts RGB color space to YCbCr color space to separate luminance from chrominance and better identify fire pixels.
3) A convolutional neural network algorithm will be used to analyze the images for fire detection. If detected, a warning will be triggered to prevent damage to forests, wildlife, and resources from wildfires.
ppt.pptx
The students are working on a project to detect forest fires using image processing techniques. They plan to take images using a digital camera and convert the images from RGB to YCbCr color space. In YCbCr, luminance and chrominance can be separated more effectively to identify fire pixels. The literature review discussed two papers on image processing methods for forest fire detection using color models like YCbCr. The goal of the project is to prevent forest fires and reduce pollution, save forests, and protect wildlife. The proposed methodology will be tested and evaluated.
project.pptx
This document summarizes a student paper presented at the 1st International Conference on Innovative Trends in Engineering and Science. The paper proposes using a convolutional neural network (CNN) for forest fire detection from images. Forest fires can damage the environment and wildlife. Current detection methods have limitations like false alarms. The students' method uses a CNN to classify images as containing fire or not. It aims to minimize pollution, save forests and resources, and prevent wildfires from growing out of control. The CNN is trained on collected images then used to detect and predict fires in new images. This helps enable early detection of flames to avoid large disasters.
zeroth review(FFD) (1).pptx
The document summarizes a project that aims to detect forest fires in the Sathyamangalam Reserve using image processing techniques. It will use the YCbCr and RGB color models to analyze images and detect fire pixels based on color. A Python code will be written and integrated into a NODE MCU to classify pixels as fire or non-fire. The algorithm will have three main stages: detecting fire pixels by color, detecting moving pixels, and analyzing dynamics of moving fire pixels over consecutive frames. The project is still in the planning stage and aims to provide early detection of forest fires without requiring sensors to directly contact the fire.
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2nd review.pptx
1) Forest fires are often caused by heat generated in litter that ignites accidentally from human carelessness in summer. Current detection methods rely on air, temperature, and particle sensors that must be triggered by particles entering the sensors.
2) The proposed method uses image processing techniques to detect fires from images captured by a digital camera. It converts RGB color space to YCbCr color space to separate luminance from chrominance and better identify fire pixels.
3) A convolutional neural network algorithm will be used to analyze the images for fire detection. If detected, a warning will be triggered to prevent damage to forests, wildlife, and resources from wildfires.
ppt.pptx
The students are working on a project to detect forest fires using image processing techniques. They plan to take images using a digital camera and convert the images from RGB to YCbCr color space. In YCbCr, luminance and chrominance can be separated more effectively to identify fire pixels. The literature review discussed two papers on image processing methods for forest fire detection using color models like YCbCr. The goal of the project is to prevent forest fires and reduce pollution, save forests, and protect wildlife. The proposed methodology will be tested and evaluated.
project.pptx
This document summarizes a student paper presented at the 1st International Conference on Innovative Trends in Engineering and Science. The paper proposes using a convolutional neural network (CNN) for forest fire detection from images. Forest fires can damage the environment and wildlife. Current detection methods have limitations like false alarms. The students' method uses a CNN to classify images as containing fire or not. It aims to minimize pollution, save forests and resources, and prevent wildfires from growing out of control. The CNN is trained on collected images then used to detect and predict fires in new images. This helps enable early detection of flames to avoid large disasters.
zeroth review(FFD) (1).pptx
The document summarizes a project that aims to detect forest fires in the Sathyamangalam Reserve using image processing techniques. It will use the YCbCr and RGB color models to analyze images and detect fire pixels based on color. A Python code will be written and integrated into a NODE MCU to classify pixels as fire or non-fire. The algorithm will have three main stages: detecting fire pixels by color, detecting moving pixels, and analyzing dynamics of moving fire pixels over consecutive frames. The project is still in the planning stage and aims to provide early detection of forest fires without requiring sensors to directly contact the fire.
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Fire usually cause serious disasters. Thus, fire detection has been an important issue to protect human life
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based on colour pair analysis and intensity level algorithm. Then, based on the above fire flame colour
features model, regions with fire-like colours are roughly separated from each frame of the test videos.
Besides segmenting fire flame regions, background objects with similar fire colours or caused by colour shift
resulted from the reflection of fire flames are also extracted from the image during the above colour
separation process. To remove these spurious fire-like regions, the image difference method and the invented
colour masking technique are applied. The device can detect fire by using Artificial Neural Network (ANN).
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Survey of local binary pattern for fire & smoke using wavelet decomposition
Abstract
Current Automatic Smoke Detection Mechanism’s, i.e. Fire Alarms, include sensor technologies, which are unreliable, as the
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The document discusses classifying kidney stone images using deep neural networks and facilitating diagnosis using IoT. Kidney stone images are acquired and preprocessed by converting to grayscale, enhancing, and segmenting the area of interest. Texture features are extracted using active contour segmentation and classified using a deep neural network model. The results, including stone type and treatment recommendations, are sent to the cloud where doctors and patients can access them, allowing automated diagnosis without human intervention.
IJRET-V1I1P1 - Forest Fire Detection Based on Wireless Image Processing
This document proposes an intelligent forest fire detection system using image processing and artificial intelligence techniques. It discusses how traditional fire detection methods are inadequate and outlines the proposed system. The system would use temperature sensors, a GPS module, and small satellites to continuously monitor forests for fires, detect fires rapidly, and transmit location alerts to aid fire station response before fires spread severely. If a temperature threshold is exceeded, the system would transmit an alert along with the fire's GPS location to orbiting small satellites and ultimately to fire stations. The goal is early and accurate fire detection and localization to minimize ecological and economic damage from forest fires.
Reversible Encrypytion and Information Concealment
Recently, a lot of attention is paid to reversible data hiding (RDH) in encrypted pictures, since it maintains the wonderful property that the initial image cover will be losslessly recovered when embedded data is extracted, whereas protects the image content that is need to be kept confidential. Other techniques used antecedently are to embed data by reversibly vacating area from the pictures, that area unit been encryted, may cause some errors on information extraction or image restoration. In this paper, we propose a unique methodology by reserving room before secret writing (i.e reserving room before encryption) with a conventional RDH algorithmic rule, and thus it becomes straightforward for hider to reversibly embed data in the encrypted image. The projected methodology is able to implement real reversibility, that is, information extraction and image recovery area unit free of any error. This methodology embedds larger payloads for constant image quality than the antecedently used techniques, like for PSNR= 40db.
Image Maximization Using Multi Spectral Image Fusion Technique
This paper reports a detailed study of a set of image fusion algorithms for its implementation. The paper explains the theory and implementation of the effective image fusion algorithm and the experimental results. Based on the research and development of some image quality metrics, the fusion algorithm is evaluated. The report is an image fusion algorithm that evaluates and implements image quality metrics that are used to evaluate the implementation. In this study, two different image fusion techniques have been applied to hyperspectral and low spatial resolution satellite images with high spatial and low spectral resolution images to obtain a fusion graph with increased spatial resolution Like, while keeping spectral information as much as possible. These techniques are raw component analysis
(PCA) and wavelet transform (WT) image fusion MATLAB is used to build the GUto
apply and render the results of image fusion algorithms. The subjective (visual) and objective evaluation of the fusion image has been carried out to assess the success of the method. The objective evaluation methods include correlation coefficient (CC), root mean square error (RMSE), relative global dimension synthesis error (ERGAS) The results show that the PCA method performs better on the top of the spectral information, and is less successful in increasing the spatial resolution. The WT is performed after the IHS transformation to improve the spatial resolution and is performed with respect to the preservation of the spectral information after the PCA and WT methods.
IRJET- Handwritten Decimal Image Compression using Deep Stacked Autoencoder
This document proposes using a deep stacked autoencoder neural network for compressing handwritten decimal image data. It involves training multiple autoencoders in sequence to form a deep network that can compress the high-dimensional input images into lower-dimensional encoded representations while minimizing information loss. The autoencoders are trained one layer at a time using scaled conjugate gradient descent. Testing on the MNIST handwritten digits dataset showed the deep stacked autoencoder achieved compression by encoding the 400-dimensional input images down to a 25-dimensional representation while maintaining good reconstruction accuracy, as measured by minimizing the mean squared error at each layer.
IRJET - An Robust and Dynamic Fire Detection Method using Convolutional N...
This document proposes a new fire detection method using convolutional neural networks (CNNs). Specifically, it uses the YOLOv3 object detection algorithm, which can detect objects like fire in images or videos quickly and accurately. The proposed method aims to reduce computational time and costs compared to other CNN-based approaches, while also improving detection accuracy and reducing false alarms. It discusses implementing the method using four main modules: data exploration, pre-processing, feature engineering, and model selection. The workflow involves exploring data, pre-processing images, extracting features, and selecting the YOLOv3 CNN model for fire detection. The goal is to develop a robust and dynamic fire detection system using computer vision techniques to help prevent accidents.
IRJET- Heuristic Approach for Low Light Image Enhancement using Deep Learning
This document discusses a deep learning approach for enhancing low light images. It begins by describing the challenges of low light imaging such as low signal-to-noise ratio and increased noise. It then reviews existing image enhancement and denoising techniques that have limitations under extreme low light conditions. The proposed approach uses a convolutional neural network trained on a dataset of low and high exposure image pairs to learn an end-to-end image processing pipeline directly from raw sensor data. This aims to better handle noise and color biases compared to traditional pipelines. The goals are to enhance short exposure images while suppressing noise and applying proper color transformations.
An Approach on Determination on Coal Quality using Digital Image Processing
Worldwide coal in the 21st century will be influenced by concerns about the effects of coal combustion on the
local, regional and global environment and on human health. Reliable coal quality data can help decision maker
to better assess the risks and limit the possible environmental degradation and impact on health. Although
millions of coal analysis have been performed worldwide, existing national quality databases are generally of
limited use because much of the data are not readily accessible; geographic coverage are not comprehensive;
analytical data may not be accurate; samples may not be representative, or current. This paper attempts to gives
a fast, accurate, user-friendly and computerized method for analysis of coal quality using techniques of digital
image processing for its better utilization.
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vj 1st review.pptx
- 1. Batch ID - FPIN7209 Internal BANNARI AMMAN INSTITUTE OF TECHNOLOGY FOREST FIRE DETECTION Special lab Name: Dr. SUNDARAMURTHY S Designation: Faculty Dept: IT Name: SARAVANA D, VIJAYA PRATHAP P, VINOTH V Reg. No: 191EE208 , 191EE247 , 191EE250 Dept: Electrical and Electronics Engineering S7 Project work – First review – 26/10/2022 S7 PROJECT WORK I - FIRST REVIEW
- 2. Problem Statement (ABSTRACT) • Forest fire caused by heat generated in litter and other biomes in summer through carelessness of people. • The most popular methods for detecting smoke from fires are typically based on air, temperature, and particle sampling transparency examination. • There is no warning triggered unless the Particles enter the sensors and turn them on. • The sensors get damaged when they place near the fire.SO we come up with the image processing technique to detect the fire with help of color models. S7 PROJECT WORK I - FIRST REVIEW
- 3. S7 PROJECT WORK I - FIRST REVIEW Literature Survey PAPER TITLE AUTHOR DESCRIPTION A Literature Study on Image Processing for Forest Fire Detection PRIYADARSHINI M HANAMARADDI M.Tech-Student Computer Science and Engineering, R V College of Engineering, Bangalore, Karnataka, India. Forests can purify water, stabilize soil, cycle nutrients, moderate climate, and store carbon. They can create habitat for wildlife and nurture environments rich in biological diversity. They can also contribute billions of dollars to the country’s economic wealth. However, hundreds of millions of hectares of forests are unfortunately devastated by forest fire each year. Forest fire has been constantly threatening to ecological systems, infrastructure, and public safety. In the image processing based forest fire detection using YCbCr colour model, method adopts rule based colour model due to its less complexity and effectiveness. YCbCr colour space effectively separates luminance from chrominance compared to other colour spaces like RGB. The method not only separates fire flame pixels but also separates high temperature fire centre pixels by taking in to account of statistical parameters of fire image in YCbCr colour space like mean and standard deviation. This paper presents a literature study on Image processing for forest fire detection. S NO 1.
- 4. S7 PROJECT WORK I - FIRST REVIEW PAPER TITLE AUTHOR DESCRIPTION Image Processing Based Forest Fire Detection Article in INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING & TECHNOLOGY · February 2012 Vipin Venugopal (National Institute of Technology-Puducherry.) A novel approach for forest fire detection using image processing technique is proposed. A rule based color model for fire pixel classification is used. The proposed algorithm uses RGB and YCbCr color space. The advantage of using YCbCr color space is that it can separate the luminance from the chrominance more effectively than RGB color space. The performance of the proposed algorithm is tested on two Sets of images, one of which contains fire; the other contains fire-like regions. Standard methods are used for calculating the performance of the algorithm. The proposed method has both higher detection rate and lower false alarm rate. Since,the algorithm is cheap in computation it can be used for real time forest fire detection. S NO 2.
- 5. S7 PROJECT WORK I - FIRST REVIEW Aim To prevent forest from wild fire and save the Environment, For the growth of nation and to save our resources from forest. Scope • To minimize Pollution. • To Save Forest. • To Prevent wild animals from extinction.
- 6. Proposed Work • In the proposed method image captured by the digital camera as input, process the image (RGB) color space convert into YCbCr color space and performs actions as shown in below figure. S7 PROJECT WORK I - FIRST REVIEW
- 7. Methodology S7 PROJECT WORK I - FIRST REVIEW
- 8. Algorithm: S7 PROJECT WORK I - ZEROTH REVIEW The Algorithm used is CNN(Convolutional Neural Network)
- 9. Working: S7 PROJECT WORK I - ZEROTH REVIEW
- 10. Output: S7 PROJECT WORK I - ZEROTH REVIEW
- 11. S7 PROJECT WORK I - FIRST REVIEW Gantt Chart
- 12. Reference : S7 PROJECT WORK I - FIRST REVIEW • https://ieeexplore.ieee.org/document/7054883#:~:text=The%20prop osed%20method%20not%20only,separate%20the%20true%20fire%2 0region. • https://www.researchgate.net/publication/288208374_Fire_recogniti on_using_RGB_and_YCbCr_color_space • https://www.researchgate.net/publication/283109204_Image_proces sing_based_forest_fire_detection_using_YCbCr_colour_model