IRJET- Automatic Traffic Sign Detection and Recognition using CNNIRJET Journal
This document presents a method for automatic traffic sign detection and recognition using convolutional neural networks (CNNs). The proposed system first enhances input images and performs thresholding and region extraction. Features are then extracted and the images are classified using a CNN. The CNN architecture includes convolutional, ReLU, pooling and fully connected layers. The system achieves detection rates over 88% mean average precision and boundary estimation errors under 3 pixels. It runs in real-time at over 7 frames per second on mobile platforms, providing accurate traffic sign detection, recognition and boundary estimation. The method is robust to occlusion, blurring and small targets compared to other methods.
This document discusses different types of biometrics used for identity verification including fingerprints, iris scans, face recognition, and voice recognition. It provides details on how each biometric works, including how fingerprints are unique and can be recognized by their binary patterns, how iriscodes scan the detailed patterns in the iris, and how voice recognition analyzes acoustic features in speech. The document also covers advantages of biometrics like security, speed, and issues to address like illumination conditions for face recognition. In summary, it is an overview of popular biometric technologies used to automatically verify identity based on physical and behavioral characteristics.
On-line handwriting recognition involves converting handwriting as it is written on a digitizer to digital text, while off-line recognition converts static images of handwriting. Both techniques face challenges from variability in handwriting styles. Current methods use feature extraction and neural networks, but do not match human-level recognition abilities. Handwriting recognition remains an important but difficult area of research.
Ce cours est une introduction au traitements informatique des images. Le traitement d'images consiste à changer la nature d'une image, afin de:
1.Améliorer de l’information contenue pour aider à l'interprétation par l'homme,
2.La rendre plus adaptée pour une perception autonome de la machine.
IRJET- Automatic Traffic Sign Detection and Recognition using CNNIRJET Journal
This document presents a method for automatic traffic sign detection and recognition using convolutional neural networks (CNNs). The proposed system first enhances input images and performs thresholding and region extraction. Features are then extracted and the images are classified using a CNN. The CNN architecture includes convolutional, ReLU, pooling and fully connected layers. The system achieves detection rates over 88% mean average precision and boundary estimation errors under 3 pixels. It runs in real-time at over 7 frames per second on mobile platforms, providing accurate traffic sign detection, recognition and boundary estimation. The method is robust to occlusion, blurring and small targets compared to other methods.
This document discusses different types of biometrics used for identity verification including fingerprints, iris scans, face recognition, and voice recognition. It provides details on how each biometric works, including how fingerprints are unique and can be recognized by their binary patterns, how iriscodes scan the detailed patterns in the iris, and how voice recognition analyzes acoustic features in speech. The document also covers advantages of biometrics like security, speed, and issues to address like illumination conditions for face recognition. In summary, it is an overview of popular biometric technologies used to automatically verify identity based on physical and behavioral characteristics.
On-line handwriting recognition involves converting handwriting as it is written on a digitizer to digital text, while off-line recognition converts static images of handwriting. Both techniques face challenges from variability in handwriting styles. Current methods use feature extraction and neural networks, but do not match human-level recognition abilities. Handwriting recognition remains an important but difficult area of research.
Ce cours est une introduction au traitements informatique des images. Le traitement d'images consiste à changer la nature d'une image, afin de:
1.Améliorer de l’information contenue pour aider à l'interprétation par l'homme,
2.La rendre plus adaptée pour une perception autonome de la machine.
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
The document discusses biometrics and biometric systems. It defines biometrics as measurable biological characteristics that can be used to identify individuals. It then describes the main components of a biometric system, including sensors, feature extraction, matching, and databases. The document discusses verification and identification modes of biometric systems. It also explains the different types of errors that can occur in biometric systems, including false accepts and false rejects, and how performance is evaluated using metrics like FMR, FNMR, FTE, and FTC rates.
Design of a hand geometry based biometric systemBhavi Bhatia
This document provides details about the design of a hand geometry-based biometric system. It discusses the methodology used, which includes image acquisition, preprocessing, feature extraction, matching, and decision stages. Image acquisition involves capturing grayscale images of hands using a digital camera. Preprocessing includes binarization to separate the hand from the background. Feature extraction measures finger lengths and widths. The extracted features are then matched against templates in a database to verify a user's identity. The overall goal is to develop a biometric verification system using geometric features of the hand.
This document discusses biometrics and its use in e-secure transactions. It defines biometrics as the automatic identification of a person based on physiological or behavioral characteristics. Some key biometrics mentioned include facial recognition, fingerprint recognition, hand geometry, iris scanning, voice recognition, signature verification, and keystroke identification. The document also outlines some applications of biometrics such as preventing unauthorized access, criminal identification, automobiles using biometrics instead of keys, and improving airport security. It concludes that biometrics is an emerging area that could replace the need for passwords, PINs, and keys in the future.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
The document discusses face recognition technology as a biometric authentication method. It describes how face recognition works by detecting nodal points on faces and creating unique faceprints. The advantages are that face recognition is convenient, socially acceptable and inexpensive compared to other biometrics. However, face recognition has difficulties with identical twins and environmental/appearance changes reducing accuracy over time. The document also outlines applications in security, law enforcement, banking, and commercial access control.
This document discusses biometrics, which uses human body characteristics to authenticate identity. It describes biometric devices that scan and digitize characteristics like fingerprints, irises, voice patterns. Biometrics can be physiological (face, fingerprints) or behavioral (signature, voice). To be used for identification, characteristics must be universal, unique, permanent, collectible, and difficult to circumvent. The document outlines various biometric modalities like fingerprint recognition, face recognition, voice recognition, and iris recognition. It also discusses advantages like accuracy but notes disadvantages like cost and changing characteristics with age, disease, or environment.
The document discusses the technology of the future. It covers several topics related to biometrics including a brief history, current applications, and the future potential of various biometric technologies such as fingerprints, iris recognition, and voice recognition. The document also discusses how biometric systems work and compares the features and accuracy of different biometric parameters.
This document outlines a course syllabus on biometrics that covers 14 topics over multiple lectures. The course introduces common biometric modalities like fingerprint, face, iris, and hand geometry recognition. It discusses the history of biometrics from ancient uses of fingerprints and handprints to modern automated systems. A typical biometric system is described as having sensors, signal processing, data storage, matching, and decision components. Characteristics of effective biometrics like universality, uniqueness, and permanence are also summarized.
Ensemble learning combines multiple machine learning models to obtain better predictive performance than could be obtained from any of the constituent models alone. It works by training base models on different subsets of the original data or using different algorithms and then combining their predictions. Two common ensemble methods are bagging and boosting. Bagging generates additional training data by sampling the original data with replacement and trains base models on these samples, while boosting iteratively reweights training examples to focus on those misclassified by previous base models. Both aim to reduce variance and prevent overfitting.
Technology that identifies you based on your physical or behavioral traits- for added security to confirm that you are who you claim to be.(this ppt is very dear to me as i have given a talk on this topic twice. this also fetched me and migmar first prize at deen dayal upadhyay college- converging vectors - an inter college presentation competition organized by arya bhata science forum)
This document discusses image segmentation techniques. It begins by introducing the goal of image segmentation as clustering pixels into salient image regions. Segmentation can be used for tasks like object recognition, image compression, and image editing. The document then discusses several bottom-up image segmentation approaches, including clustering pixels in feature space using mixtures of Gaussians models or K-means, mean-shift segmentation which models feature density non-parametrically, and graph-based segmentation methods which construct similarity graphs between pixels. It provides examples and discusses assumptions and limitations of each approach. The key approaches discussed are clustering in feature space, mean-shift segmentation, and graph-based similarity methods like the local variation algorithm.
This document describes a project on age and gender detection using deep learning and convolutional neural networks. The objectives are to pretrain a model using the UTKFace dataset to detect age and gender from facial images. The methodology involves data preprocessing like grayscale conversion, resizing, normalization. A CNN model is built with convolutional blocks and fully connected layers. The model is compiled with binary cross entropy loss for gender classification and mean absolute error for age detection. The trained model is tested and deployed using streamlit. Real-world use cases discussed are advertising based on targeted audiences and an Android app that detects age from photos.
This document describes a fingerprint authentication system for ATMs. It discusses capturing fingerprint images using an optical sensor, extracting minutiae features like ridge endings and bifurcations, and matching fingerprints by comparing minutiae triplets. The system aims to provide biometric security for ATM transactions by verifying a user's identity based on their fingerprint and PIN code. It proposes encrypting fingerprint images during transmission and extracting encryption keys from the images to protect biometric data.
Noise in images can take various forms and have different sources. Gaussian noise follows a normal distribution and looks like subtle color variations, while salt and pepper noise completely replaces some pixel values with maximum or minimum values. Mean, median, and trimmed filters are commonly used to reduce noise. Mean filters average pixel values within a window, but can blur details. Median filters replace the center pixel with the median value in the window, which is effective for salt and pepper noise while retaining details better than mean filters. Adaptive filters vary the window size to better target noise without excessive blurring.
Biometric system is a pattern identification system that recognizes an individual by determining the originality of the physical features and behavioral characteristic of that person. Of all the recently used biometric techniques, fingerprint identification systems have gained the most popularity because of the prolonged existence of fingerprints and its extensive use. Fingerprint is dependable biometric trait as it is an idiosyncratic and dedicated. It is a technology that is increasingly used in various fields like forensics and security purpose. The vital objective of our system is to make ATM transaction more secure and user friendly. This system replaces traditional ATM cards with fingerprint. Therefore, there is no need to carry ATM cards to perform transactions. The money transaction can be made more secure without worrying about the card to be lost. In our system we are using embedded system with biometrics i.e r305 sensor and UART microcontroller. The Fingerprint and the user_id of all users are stored in the database. Fingerprints are used to identify whether the Person is genuine. A Fingerprint scanner is used to acquire the fingerprint of the individual, after which the system requests for the PIN (Personal Identification Number). The user gets three chances to get him authenticated. If the fingerprints do not match further authentication will be needed. After the verification with the data stored in the system database, the user is allowed to make transactions.
This document summarizes a project that aims to develop a software system using computer vision and machine learning to detect whether motorcycle riders in Bangladesh are wearing helmets. It will use a camera to take photos of riders, apply object detection models like YOLO to identify bikes and people, and check if the riders are wearing helmets. If not, it will record the bike's license plate number. The document reviews similar existing works and compares the parameters of this project. It outlines that the project will be implemented in Python using YOLO and OpenCV for real-time object detection and helmet detection from images to help enforce road safety in Bangladesh.
Version DRAFT d'une formation Data Scientist que j'ai conçue à partir de sources diverses (voir références bibliographiques à la fin de chaque diapositive).
La formation est destinée aux personnes possédant des bases (~BAC+2) en statistiques et programmation (j'utilise R).
Je reste ouvert à tout commentaire, critique et correction. Je continuerai à mettre à jour les diapositives et à en ajouter d'autres si j'ai le temps.
This document discusses fingerprint recognition using minutiae-based features. It describes the key stages of fingerprint recognition as pre-processing, minutiae extraction, and post-processing. The pre-processing stage involves image acquisition, enhancement, binarization, and segmentation. Minutiae extraction identifies features like ridge endings and bifurcations. Post-processing performs matching and verification of minutiae features between fingerprints. The document provides details on each stage and techniques used for minutiae-based fingerprint recognition.
Background subtraction is a technique used to separate foreground objects from backgrounds in video frames. It works by comparing each frame to a background model and detecting differences which indicate moving foreground objects. Recursive techniques like mixtures of Gaussians model the background pixel values over time using multiple Gaussian distributions, allowing the background model to adapt to changing lighting conditions. Adaptive background/foreground detection uses a background model that evolves over time to distinguish foreground objects from the background in a robust way.
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
The document discusses biometrics and biometric systems. It defines biometrics as measurable biological characteristics that can be used to identify individuals. It then describes the main components of a biometric system, including sensors, feature extraction, matching, and databases. The document discusses verification and identification modes of biometric systems. It also explains the different types of errors that can occur in biometric systems, including false accepts and false rejects, and how performance is evaluated using metrics like FMR, FNMR, FTE, and FTC rates.
Design of a hand geometry based biometric systemBhavi Bhatia
This document provides details about the design of a hand geometry-based biometric system. It discusses the methodology used, which includes image acquisition, preprocessing, feature extraction, matching, and decision stages. Image acquisition involves capturing grayscale images of hands using a digital camera. Preprocessing includes binarization to separate the hand from the background. Feature extraction measures finger lengths and widths. The extracted features are then matched against templates in a database to verify a user's identity. The overall goal is to develop a biometric verification system using geometric features of the hand.
This document discusses biometrics and its use in e-secure transactions. It defines biometrics as the automatic identification of a person based on physiological or behavioral characteristics. Some key biometrics mentioned include facial recognition, fingerprint recognition, hand geometry, iris scanning, voice recognition, signature verification, and keystroke identification. The document also outlines some applications of biometrics such as preventing unauthorized access, criminal identification, automobiles using biometrics instead of keys, and improving airport security. It concludes that biometrics is an emerging area that could replace the need for passwords, PINs, and keys in the future.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
The document discusses face recognition technology as a biometric authentication method. It describes how face recognition works by detecting nodal points on faces and creating unique faceprints. The advantages are that face recognition is convenient, socially acceptable and inexpensive compared to other biometrics. However, face recognition has difficulties with identical twins and environmental/appearance changes reducing accuracy over time. The document also outlines applications in security, law enforcement, banking, and commercial access control.
This document discusses biometrics, which uses human body characteristics to authenticate identity. It describes biometric devices that scan and digitize characteristics like fingerprints, irises, voice patterns. Biometrics can be physiological (face, fingerprints) or behavioral (signature, voice). To be used for identification, characteristics must be universal, unique, permanent, collectible, and difficult to circumvent. The document outlines various biometric modalities like fingerprint recognition, face recognition, voice recognition, and iris recognition. It also discusses advantages like accuracy but notes disadvantages like cost and changing characteristics with age, disease, or environment.
The document discusses the technology of the future. It covers several topics related to biometrics including a brief history, current applications, and the future potential of various biometric technologies such as fingerprints, iris recognition, and voice recognition. The document also discusses how biometric systems work and compares the features and accuracy of different biometric parameters.
This document outlines a course syllabus on biometrics that covers 14 topics over multiple lectures. The course introduces common biometric modalities like fingerprint, face, iris, and hand geometry recognition. It discusses the history of biometrics from ancient uses of fingerprints and handprints to modern automated systems. A typical biometric system is described as having sensors, signal processing, data storage, matching, and decision components. Characteristics of effective biometrics like universality, uniqueness, and permanence are also summarized.
Ensemble learning combines multiple machine learning models to obtain better predictive performance than could be obtained from any of the constituent models alone. It works by training base models on different subsets of the original data or using different algorithms and then combining their predictions. Two common ensemble methods are bagging and boosting. Bagging generates additional training data by sampling the original data with replacement and trains base models on these samples, while boosting iteratively reweights training examples to focus on those misclassified by previous base models. Both aim to reduce variance and prevent overfitting.
Technology that identifies you based on your physical or behavioral traits- for added security to confirm that you are who you claim to be.(this ppt is very dear to me as i have given a talk on this topic twice. this also fetched me and migmar first prize at deen dayal upadhyay college- converging vectors - an inter college presentation competition organized by arya bhata science forum)
This document discusses image segmentation techniques. It begins by introducing the goal of image segmentation as clustering pixels into salient image regions. Segmentation can be used for tasks like object recognition, image compression, and image editing. The document then discusses several bottom-up image segmentation approaches, including clustering pixels in feature space using mixtures of Gaussians models or K-means, mean-shift segmentation which models feature density non-parametrically, and graph-based segmentation methods which construct similarity graphs between pixels. It provides examples and discusses assumptions and limitations of each approach. The key approaches discussed are clustering in feature space, mean-shift segmentation, and graph-based similarity methods like the local variation algorithm.
This document describes a project on age and gender detection using deep learning and convolutional neural networks. The objectives are to pretrain a model using the UTKFace dataset to detect age and gender from facial images. The methodology involves data preprocessing like grayscale conversion, resizing, normalization. A CNN model is built with convolutional blocks and fully connected layers. The model is compiled with binary cross entropy loss for gender classification and mean absolute error for age detection. The trained model is tested and deployed using streamlit. Real-world use cases discussed are advertising based on targeted audiences and an Android app that detects age from photos.
This document describes a fingerprint authentication system for ATMs. It discusses capturing fingerprint images using an optical sensor, extracting minutiae features like ridge endings and bifurcations, and matching fingerprints by comparing minutiae triplets. The system aims to provide biometric security for ATM transactions by verifying a user's identity based on their fingerprint and PIN code. It proposes encrypting fingerprint images during transmission and extracting encryption keys from the images to protect biometric data.
Noise in images can take various forms and have different sources. Gaussian noise follows a normal distribution and looks like subtle color variations, while salt and pepper noise completely replaces some pixel values with maximum or minimum values. Mean, median, and trimmed filters are commonly used to reduce noise. Mean filters average pixel values within a window, but can blur details. Median filters replace the center pixel with the median value in the window, which is effective for salt and pepper noise while retaining details better than mean filters. Adaptive filters vary the window size to better target noise without excessive blurring.
Biometric system is a pattern identification system that recognizes an individual by determining the originality of the physical features and behavioral characteristic of that person. Of all the recently used biometric techniques, fingerprint identification systems have gained the most popularity because of the prolonged existence of fingerprints and its extensive use. Fingerprint is dependable biometric trait as it is an idiosyncratic and dedicated. It is a technology that is increasingly used in various fields like forensics and security purpose. The vital objective of our system is to make ATM transaction more secure and user friendly. This system replaces traditional ATM cards with fingerprint. Therefore, there is no need to carry ATM cards to perform transactions. The money transaction can be made more secure without worrying about the card to be lost. In our system we are using embedded system with biometrics i.e r305 sensor and UART microcontroller. The Fingerprint and the user_id of all users are stored in the database. Fingerprints are used to identify whether the Person is genuine. A Fingerprint scanner is used to acquire the fingerprint of the individual, after which the system requests for the PIN (Personal Identification Number). The user gets three chances to get him authenticated. If the fingerprints do not match further authentication will be needed. After the verification with the data stored in the system database, the user is allowed to make transactions.
This document summarizes a project that aims to develop a software system using computer vision and machine learning to detect whether motorcycle riders in Bangladesh are wearing helmets. It will use a camera to take photos of riders, apply object detection models like YOLO to identify bikes and people, and check if the riders are wearing helmets. If not, it will record the bike's license plate number. The document reviews similar existing works and compares the parameters of this project. It outlines that the project will be implemented in Python using YOLO and OpenCV for real-time object detection and helmet detection from images to help enforce road safety in Bangladesh.
Version DRAFT d'une formation Data Scientist que j'ai conçue à partir de sources diverses (voir références bibliographiques à la fin de chaque diapositive).
La formation est destinée aux personnes possédant des bases (~BAC+2) en statistiques et programmation (j'utilise R).
Je reste ouvert à tout commentaire, critique et correction. Je continuerai à mettre à jour les diapositives et à en ajouter d'autres si j'ai le temps.
This document discusses fingerprint recognition using minutiae-based features. It describes the key stages of fingerprint recognition as pre-processing, minutiae extraction, and post-processing. The pre-processing stage involves image acquisition, enhancement, binarization, and segmentation. Minutiae extraction identifies features like ridge endings and bifurcations. Post-processing performs matching and verification of minutiae features between fingerprints. The document provides details on each stage and techniques used for minutiae-based fingerprint recognition.
Background subtraction is a technique used to separate foreground objects from backgrounds in video frames. It works by comparing each frame to a background model and detecting differences which indicate moving foreground objects. Recursive techniques like mixtures of Gaussians model the background pixel values over time using multiple Gaussian distributions, allowing the background model to adapt to changing lighting conditions. Adaptive background/foreground detection uses a background model that evolves over time to distinguish foreground objects from the background in a robust way.
Scaling Eventbrite to $1B - Presented at Dublin Web Summit 2012 by Co-founder...Renaud Visage
Renaud Visage is the co-founder and CTO of Eventbrite, an event ticketing and discovery platform. He discusses how Eventbrite scaled from its founding in 2006 with one engineer to processing over $1 billion in ticket sales. Key steps included upgrading infrastructure, adding redundancy, automating processes, and hiring specialists as the company grew. Lessons learned include expecting the unexpected, welcoming challenges, and having fun along the way.
This document provides information on a 40-year-old female patient admitted for J-tube placement due to severe protein-energy malnutrition. She has a complex surgical history including gastrectomy and small bowel resections which has resulted in nutritional deficiencies. Laboratory results show low albumin, prealbumin, calcium and magnesium levels indicative of her malnutrition. The patient is started on continuous tube feedings which are advanced gradually, however her blood sugars remain difficult to control when eating orally in addition to the tube feedings.
Projet de fin d'etude :Control d’acces par empreintes digitaleAbdo07
Projet de fin d'etude :Control d’acces par empreintes digitale
Réalisé par : AABIDA Abderrahime _NAJMA Soufiane _ AIT BBA Mohamed
Encadré par : M.ROUFI
Année Universitaire : 2014-2015
Université Cadi Ayyad
Faculté des sciences Semlalia
Marrakech
Face Recognition using PCA-Principal Component Analysis using MATLABSindhi Madhuri
PCA is used for face recognition. It involves calculating eigenvectors from a training set of face images to define a feature space called "eigenfaces". A new face is recognized by projecting it onto this space and comparing to existing faces. PCA works by identifying directions of maximum variance in the training data, capturing the most important information about faces with fewer vectors. Potential applications include identification, security, and human-computer interaction. However, it is sensitive to changes in lighting and expression.
This document is a marketing plan for David's Tea, a Canadian tea retailer with over 100 stores. It outlines the company's mission, goals, strengths, customer base, competitors, and proposed marketing strategies. David's Tea aims to provide high quality loose-leaf teas and exceptional customer service in a fun atmosphere. Their strengths include a strong brand, unique tea blends, frequent new product releases, knowledgeable staff, and customization options. The plan analyzes competitors like Starbucks and Tim Hortons and proposes strategies for products, pricing, promotion, and distribution to continue David's Tea's expansion.
Alphorm.com Support de la Formation Les Sciences Forensiques : L’investigati...Alphorm
Formation complète ici :
http://www.alphorm.com/tutoriel/formation-en-ligne-les-sciences-forensiques-l-investigation-numerique
Le nombre d'attaques explosent depuis quelques années, l'investigation réseau est un besoin grandissant au sein des entreprises et pour les analystes inforensiques afin d'identifier la source de l'intrusion et les canaux cachés mis en place par les attaquants pour exfiltrer des informations sensibles.
En effet, l’investigation numérique dérive du terme anglais Computer forensics c'est-à-dire l’utilisation de techniques spécialisées dans la collecte, l’identification, la description, la sécurisation, l’extraction, l’authentification, l’analyse, l’interprétation et l’explication de l’information numérique.
Votre MVP Hamza KONDAH vous a préparé cette formation sur les sciences forensiques pour l’investigation numérique qui présente, de façon détaillée, les points importants à connaître pour mener à bien des études inforensiques complètes.
La formation sur les sciences forensiques pour l’investigation numérique vous donnera les qualifications nécessaires pour identifier les traces laissées lors de l’intrusion d’un système informatique par un tiers ainsi que d’effectuer de l’investigation sur des supports numériques.
Avec cette formation sur les sciences forensiques pour l’investigation numérique, vous allez apprendre à collecter correctement les preuves nécessaires à des poursuites judiciaires mais aussi d’acquérir de l’expérience sur les différentes techniques d’investigation et l’acquisition de preuve virtuelle, dans la gestion et l’analyse de façon légale.
Illustrée par de nombreuses démonstrations, cette formation sur les sciences forensiques pour l’investigation numérique est interactive et permet de découvrir la marche à suivre et tous les outils nécessaires pour être prêt à étudier efficacement toute situation : intrusion, fuite d'information, actions malveillantes d'un employé curieux.
A FILOSOFIA DA CIÊNCIA DE KARL POPPER: O RACIONALISMO CRÍTICOKarol Souza
1) O documento discute a filosofia da ciência de Karl Popper, especificamente seu racionalismo crítico.
2) Popper criticou a visão positivista de que o conhecimento pode ser justificado pela indução, apontando que não há lógica indutiva.
3) Ele propôs um método crítico de teste dedutivo, onde teorias são conjecturas testadas através de suas consequências deduzidas, podendo ser falseadas, mas não provadas como verdadeiras.
Este documento presenta el programa de la asignatura estatal "Aprender a aprender en la escuela secundaria duranguense" para los ciclos escolares 2010-2011 y 2011-2012. El programa se enfoca en desarrollar estrategias que permitan a los estudiantes aprender a aprender de manera autónoma a lo largo de su vida. Consta de cinco bloques temáticos que abordan el aprendizaje del estudiante duranguense, la importancia de la lectura, la escritura, el uso de tecnologías de la información y la comunicación
Este documento describe la evolución del concepto de grupo étnico. Inicialmente, etnia se usaba como sinónimo de raza. Luego, se diferenció de raza para referirse a rasgos culturales en lugar de biológicos. Actualmente, un grupo étnico se define como una comunidad sociocultural que comparte rasgos culturales, físicos o lingüísticos, tiene conciencia de pertenencia e interacciona con otros grupos. Los rasgos diacríticos como origen, lengua o religión sirven para marcar la pertenencia y diferenci
This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works by detecting nodal points on faces and creating faceprints for identification. It also covers implementations, comparing images to templates to verify or identify individuals, and applications in security and surveillance. Strengths are its non-invasive nature, but it can be impacted by changes in appearance.
O documento apresenta um resumo de uma aula sobre gestão de recursos humanos. Aborda temas como avaliação de desempenho, métodos de avaliação e a importância dos processos de recursos humanos para a organização.
Seven habits of highly effective people ziane bilalBilal ZIANE
This document summarizes a presentation about Stephen Covey's book "The Seven Habits of Highly Effective People". It provides background on Covey and an overview of the seven habits: 1) Be Proactive, 2) Begin with the End in Mind, 3) Put First Things First, 4) Think Win-Win, 5) Seek First to Understand Then to Be Understood, 6) Synergize, and 7) Sharpen the Saw. The presentation was given by Ziane Bilal at Abdelmalek Essaâdi University in Tangier, Morocco to provide an introduction to Covey's principles of effectiveness.
The document discusses cloud computing and Eucalyptus, an open-source software for building private and public clouds. It provides an overview of cloud computing models, benefits and challenges. It then describes the architecture and components of Eucalyptus, including the cluster controller, node controller and cloud controller. Sample setup configurations are also discussed. Finally, it compares Eucalyptus to other cloud software like OpenNebula and Nimbus.
The document discusses Eucalyptus, an open-source software for building private and hybrid clouds. It begins with an introduction to cloud computing models (SaaS, PaaS, IaaS) and types of clouds (public, private, hybrid). It then covers the role of grid computing in cloud computing. Eucalyptus components and setup are presented, followed by a comparison of cloud software like OpenNebula, Nimbus, OpenStack, CloudStack and Eucalyptus. The document concludes with a demonstration of using Eucalyptus.
Cycle de Formation Théâtrale 2024 / 2025Billy DEYLORD
Pour la Saison 2024 / 2025, l'association « Le Bateau Ivre » propose un Cycle de formation théâtrale pour particuliers amateurs et professionnels des arts de la scène enfants, adolescents et adultes à l'Espace Saint-Jean de Melun (77). 108 heures de formation, d’octobre 2024 à juin 2025, à travers trois cours hebdomadaires (« Pierrot ou la science de la Scène », « Montage de spectacles », « Le Mime et son Répertoire ») et un stage annuel « Tournez dans un film de cinéma muet ».
Newsletter SPW Agriculture en province du Luxembourg du 12-06-24BenotGeorges3
Les informations et évènements agricoles en province du Luxembourg et en Wallonie susceptibles de vous intéresser et diffusés par le SPW Agriculture, Direction de la Recherche et du Développement, Service extérieur de Libramont.
Le fichier :
Les newsletters : https://agriculture.wallonie.be/home/recherche-developpement/acteurs-du-developpement-et-de-la-vulgarisation/les-services-exterieurs-de-la-direction-de-la-recherche-et-du-developpement/newsletters-des-services-exterieurs-de-la-vulgarisation/newsletters-du-se-de-libramont.html
Bonne lecture et bienvenue aux activités proposées.
#Agriculture #Wallonie #Newsletter #Recherche #Développement #Vulgarisation #Evènement #Information #Formation #Innovation #Législation #PAC #SPW #ServicepublicdeWallonie
Formation M2i - Onboarding réussi - les clés pour intégrer efficacement vos n...M2i Formation
Améliorez l'intégration de vos nouveaux collaborateurs grâce à notre formation flash sur l'onboarding. Découvrez des stratégies éprouvées et des outils pratiques pour transformer l'intégration en une expérience fluide et efficace, et faire de chaque nouvelle recrue un atout pour vos équipes.
Les points abordés lors de la formation :
- Les fondamentaux d'un onboarding réussi
- Les outils et stratégies pour un onboarding efficace
- L'engagement et la culture d'entreprise
- L'onboarding continu et l'amélioration continue
Formation offerte animée à distance avec notre expert Eric Collin
Impact des Critères Environnementaux, Sociaux et de Gouvernance (ESG) sur les...mrelmejri
J'ai réalisé ce projet pour obtenir mon diplôme en licence en sciences de gestion, spécialité management, à l'ISCAE Manouba. Au cours de mon stage chez Attijari Bank, j'ai été particulièrement intéressé par l'impact des critères Environnementaux, Sociaux et de Gouvernance (ESG) sur les décisions d'investissement dans le secteur bancaire. Cette étude explore comment ces critères influencent les stratégies et les choix d'investissement des banques.
Conseils pour Les Jeunes | Conseils de La Vie| Conseil de La JeunesseOscar Smith
Besoin des conseils pour les Jeunes ? Le document suivant est plein des conseils de la Vie ! C’est vraiment un document conseil de la jeunesse que tout jeune devrait consulter.
Voir version video:
➡https://youtu.be/7ED4uTW0x1I
Sur la chaine:👇
👉https://youtube.com/@kbgestiondeprojets
Aimeriez-vous donc…
-réussir quand on est jeune ?
-avoir de meilleurs conseils pour réussir jeune ?
- qu’on vous offre des conseils de la vie ?
Ce document est une ressource qui met en évidence deux obstacles qui empêchent les jeunes de mener une vie épanouie : l'inaction et le pessimisme.
1) Découvrez comment l'inaction, c'est-à-dire le fait de ne pas agir ou d'agir alors qu'on le devrait ou qu'on est censé le faire, est un obstacle à une vie épanouie ;
> Comment l'inaction affecte-t-elle l'avenir du jeune ? Que devraient plutôt faire les jeunes pour se racheter et récupérer ce qui leur appartient ? A découvrir dans le document ;
2) Le pessimisme, c'est douter de tout ! Les jeunes doutent que la génération plus âgée ne soit jamais orientée vers la bonne volonté. Les jeunes se sentent toujours mal à l'aise face à la ruse et la volonté politique de la génération plus âgée ! Cet état de doute extrême empêche les jeunes de découvrir les opportunités offertes par les politiques et les dispositifs en faveur de la jeunesse. Voulez-vous en savoir plus sur ces opportunités que la plupart des jeunes ne découvrent pas à cause de leur pessimisme ? Consultez cette ressource gratuite et profitez-en !
En rapport avec les " conseils pour les jeunes, " cette ressource peut aussi aider les internautes cherchant :
➡les conseils pratiques pour les jeunes
➡conseils pour réussir
➡jeune investisseur conseil
➡comment investir son argent quand on est jeune
➡conseils d'écriture jeunes auteurs
➡conseils pour les jeunes auteurs
➡comment aller vers les jeunes
➡conseil des jeunes citoyens
➡les conseils municipaux des jeunes
➡conseils municipaux des jeunes
➡conseil des jeunes en mairie
➡qui sont les jeunes
➡projet pour les jeunes
➡conseil des jeunes paris
➡infos pour les jeunes
➡conseils pour les jeunes
➡Quels sont les bienfaits de la jeunesse ?
➡Quels sont les 3 qualités de la jeunesse ?
➡Comment gérer les problèmes des adolescents ?
➡les conseils de jeunes
➡guide de conseils de jeunes
1. Université Abdelmalek Essaâdi
Faculté des sciences et Techniques
Tanger
Présenté à :
M. AIT KBIR M'hamed.
Réalisé par :
ASSAOUY Samia.
EL KHECHYNE Sarah.
ZIANE Bilal.
2. Plan
INTRODUCTION.
I. Biométrie et classification des empreintes digitales
1. Biométrie.
2. L’utilisation de la biométrie.
3. Qu’est ce qu’une empreinte digitale?
4. Forme générale.
5. Minuties.
I.Identification des empreintes digitales
1. Capteurs d’empreintes digitales.
2. Traitement d’une empreinte digitale.
3. Comparaison d’une empreinte digitale.
I.Etude de cas
1. Principe.
2. Algorithme d’application.
CONCLUSION.
08/03/12
5. Biométrie
• L’usage de différentes caractéristiques
physiologiques et comportementales afin
de réaliser une reconnaissance sure et
automatique d’un individu.
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6. L’utilisation de la biométrie
• Le contrôle d'accès à des locaux;
• Les systèmes d'information;
• La police et les gouvernements;
• Les documents officiels;
• L'automobile.
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7. Qu’est ce qu’une empreinte digitale?
• C’est la surface de la peau des
doigts pourvue d’une texture
particulière, continuellement striée
par des crêtes.
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8. Forme générale
• empreinte en boucle
• empreinte en verticille
• empreinte en arc
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15. Identification des empreintes digitales.
- Traitement d’une empreinte digitale
1. Stockage de l'empreinte
2. Filtrage des images
3. Squelettisation de l'image(binaire)
4. Extraction des minuties
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19. Champ d’orientation
• Le champ d'orientation d'une image
d'empreinte digitale représente la nature
intrinsèque d’empreinte digitale.
• C’est un étage essentielle pour déterminer
les rides d’empreinte digitale et pour trouver
la région d’intérêt d’image d’empreinte
digitale.
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L'avantage de l'identification biométrique est que chaque individu a ses propres caractéristiques physiques qui ne peuvent être changées, perdues ou volées.
Le contrôle d'accès à des locaux (sites sensibles, salles informatiques...), Les systèmes d'information (lancement du système d'exploitation, accès au réseau, commerce électronique...), La police et les gouvernements (services d’immigration, aéroports, manifestations...), Les documents officiels (fichiers judiciaires, titres d'identités, votes électroniques...), L'automobile (système d'ouverture et de démarrage sans clé).
Elles peuvent donc être utilisées pour identifier une personne. Ces traces, appelées empreintes, sont uniques et caractéristiques de chaque individu. Même les vrais jumeaux présentent des empreintes digitales différentes
Charge-Coupled Device, ou dispositif à transfert de charge
La mesure se fait par contact entre le doigts et un réseau d’élements sensibles
Mesure pas la différence de température de la peau des crêtes et vallées L’utilisateur balaye son doigt à la surface du capteur Balaye le doigt = éviter l’équilibre thermique
système de comparaison qui soit insensible à d’éventuelles translations, rotations et déformations qui affectent systématiquement les empreintes digitales. A partir de deux ensembles de minuties extraites, le système est capable de donner un indice de similitude ou de correspondance qui vaut : 0 % si les empreintes sont totalement différentes. 100 % si les empreintes viennent de la même image.
Dans cette étude nous avons présenté une vue générale de la biométrie et quelques caractéristiques des empreintes digitales. Ainsi que les techniques de l’identification et la comparaison des empreintes accompagnées d’une étude de cas démontrant les démarches à suivre pour réaliser un programme identificateur des empreintes digitales.