2nd review.pptx
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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.
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vj 1st review.pptx
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.
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
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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.
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.
Face recognition using gaussian mixture model & artificial neural network
Abstract
Face recognition is a non-contact and friendly biometric identification technology. It has broad application prospects in the
military, public security and economic security. In this work, we also consider illumination variable database. The images have
taken from far distance and do not consider the close view face of the individual as in most of the face databases, clear face view
has been considered. In this first we located face as region of interest and then LBP and LPQ descriptors are used which is
illuminance invariant in nature. After this GMM has been used to reduce feature set by taking negative log-likelihood from each
LBP and LPQ descripted image histograms. After this ANN consumes stayed used for organization purposes. The investigational
consequencesshow excellent correctness rates in overall testing of input data.
Keywords: Illumination invariant, face recognition, LBP, LPQs,GMM,ANN
An improved hdr image processing using fast global
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An improved hdr image processing using fast global tone mapping
Abstract People always try to reconstruct images that look like original scene. But it is not succeeded up to the expected level because the display devices cannot accommodate the actual range of illumination. The original scene is having high dynamic range (HDR) and the display devices are having low dynamic range (LDR) of values. Tone mapping can be used to display HDR image in an LDR device. In this paper we are trying to develop a better tone mapping method. A number of images with different exposure are fused to form an HDR image.. Here we used average method for this which is the simplest one. Then by using tone mapping the HDR image is converted into LDR. We tried Tone Reproduction Curve (TRC) based global tone mapping. TRC method results faster operation The global tone mapping operator algorithm is simple and does not introduce ghosting. The image will also free from blur and halo like artifacts. We took log average of the images to keep the pixel values within limit. Index Terms: Image fusion, High dynamic range images, , Tone mapping. Low dynamic range image, global operator.
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定制美国西雅图城市大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【美国西雅图城市大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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一比一原版(LSBU毕业证书)伦敦南岸大学毕业证如何办理
学校原件一模一样【微信：6496090 】【(LSBU毕业证书)伦敦南岸大学毕业证成绩单】【微信：6496090 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微6496090
【主营项目】
一.毕业证【q微6496090】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微6496090】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(LSBU毕业证书)伦敦南岸大学毕业证【微信：6496090 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(LSBU毕业证书)伦敦南岸大学毕业证【微信：6496090 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(LSBU毕业证书)伦敦南岸大学毕业证【微信：6496090 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(LSBU毕业证书)伦敦南岸大学毕业证【微信：6496090 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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Architectural and constructions management experience since 2003 including 18 years located in UAE.
Coordinate and oversee all technical activities relating to architectural and construction projects,
including directing the design team, reviewing drafts and computer models, and approving design
changes.
Organize and typically develop, and review building plans, ensuring that a project meets all safety and
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Consulting with clients, work on formulating equipment and labor cost estimates, ensuring a project
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本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版(BU毕业证)波士顿大学毕业证如何办理
BU毕业证学位证【微信95270640】办理BU毕业证【Q微信95270640】波士顿大学毕业证书原版↑制作波士顿大学学历认证文凭办理波士顿大学留信网认证,留学回国办理毕业证成绩单文凭学历认证【Q微信95270640】专业为海外学子办理毕业证成绩单、文凭制作，学历仿制，回国人员证明、做文凭，研究生、本科、硕士学历认证、留信认证、结业证、学位证书样本、美国教育部认证百分百真实存档可查】
【实体公司】办波士顿大学波士顿大学本科毕业证成绩单学历认证学位证文凭认证办留信网认证办留服认证办教育部认证（网上可查实体公司专业可靠）
— — — 留学归国服务中心 — — -
【主营项目】
一.波士顿大学毕业证成绩单使馆认证教育部认证成绩单等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
国外毕业证学位证成绩单办理流程：
1客户提供波士顿大学波士顿大学本科毕业证成绩单办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
专业服务请勿犹豫联系我！本公司是留学创业和海归创业者们的桥梁。一次办理终生受用一步到位高效服务。详情请在线咨询办理,欢迎有诚意办理的客户咨询!洽谈。
招聘代理：本公司诚聘英国加拿大澳洲新西兰美国法国德国新加坡各地代理人员如果你有业余时间有兴趣就请联系我们咨询顾问：+微信:95270640知道母亲身体不好家里盖新房也欠了不少钱总想趁假期赚点钱在校寄宿时用不着老向爷爷奶奶要盛夏的乡村仍旧清凉清清爽爽的山娃也过得自由自在不知为啥山娃总情不自禁地思念起城里的父亲每年暑假瞅见远乡近邻的小伙伴都争先恐后地往城里跑山娃就更思念父亲了老想着进父亲的城看看每次从城里洋里洋气地回来小伙伴们总争论着各自到过的城比试比试谁父亲的城最大最美他们大谈城里的新鲜事大谈父亲携他们逛城的快乐事在孩子们幼小的心中地
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2nd 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 – Second review – 27/10/2022 S7 PROJECT WORK I - SECOND 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 - SECOND REVIEW
- 3. S7 PROJECT WORK I - SECOND 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 - SECOND 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 - SECOND 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 - SECOND REVIEW
- 7. Methodology S7 PROJECT WORK I - SECOND REVIEW
- 8. Algorithm: S7 PROJECT WORK I - SECOND REVIEW The Algorithm used is CNN(Convolutional Neural Network)
- 9. Working: S7 PROJECT WORK I - SECOND REVIEW
- 10. Output: S7 PROJECT WORK I - SECOND REVIEW
- 11. S7 PROJECT WORK I - SECOND REVIEW Gantt Chart
- 12. Reference : S7 PROJECT WORK I - SECOND 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