GUI based Face detection using Viola-Jones algorithm in MATLAB.Binita Khua
The document discusses the Viola-Jones face detection algorithm. It outlines how the algorithm uses Haar-like features and an integral image to quickly detect faces in images. The Viola-Jones algorithm searches an image with sliding boxes and detects Haar-like features within each box, such as edges, lines, and four-sided shapes. An integral image allows for fast calculation of Haar-like feature values by summing pixel values within regions. Together, Haar-like features and the integral image allow the Viola-Jones algorithm to efficiently detect the presence of faces in images.
Avihu Efrat's Viola and Jones face detection slideswolf
The document summarizes the Viola-Jones object detection framework. It uses a cascade of classifiers with increasingly more complex features trained with AdaBoost to rapidly detect objects. Integral images allow for very fast feature evaluations. The framework was applied to face detection, achieving very fast average detection speeds of 270 microseconds per sub-window while maintaining low false positive rates.
Rapid object detection using boosted cascade of simple featuresHirantha Pradeep
1. The document presents the seminal work of Viola and Jones on rapid object detection using boosted cascades of simple features.
2. It introduces integral images for fast feature evaluation and uses AdaBoost for feature selection and classifier training in a cascade structure.
3. The cascade approach combines classifiers such that earlier ones rapidly reject negatives while later ones focus on positives, achieving real-time detection rates.
1. The document presents the Viola-Jones object detection framework which uses integral images, AdaBoost learning, and a cascade classifier structure for real-time object detection, such as face detection.
2. It introduces integral images as an image representation for fast feature extraction, AdaBoost for selecting important features, and a cascade structure for increased detection speed while maintaining accuracy.
3. Experiments showed the cascade classifier approach was nearly 10 times faster than a single classifier while maintaining high detection rates, enabling real-time face detection.
1. The document summarizes the robust real-time face detection method proposed by Viola and Jones in 2002, which uses integral images for fast feature computation, AdaBoost for feature selection, and a cascade structure for real-time processing.
2. It describes how integral images allow computing rectangular features in constant time, and how AdaBoost selects the most discriminative features by iteratively assigning higher weights to misclassified examples.
3. Finally, it explains that the cascade structure filters out most negative sub-windows using simple classifiers at the top, focusing computation only on the few potentially positive windows.
Robust Real-time Face Detection by Paul Viola and Michael Jones addresses three challenges:
1. Rapidly computing features using an integral image representation
2. Selecting discriminative features using AdaBoost
3. Achieving real-time performance through a cascade of classifiers that filter non-face regions with simple classifiers before more complex analysis.
Gaze transformers use vision transformers for gaze estimation from facial images. A hybrid model combines a CNN for image features with a transformer. It outperforms pure transformer and CNN models. Ablation studies show removing self-attention or convolutional layers hurts performance. Pre-training on a large dataset helps transformers achieve state-of-the-art results, and future methods may rely more on pre-training for gaze estimation tasks.
hands on machine learning Chapter 4 model trainingJaey Jeong
This document summarizes key concepts from a chapter on model training in machine learning, including linear regression, gradient descent, polynomial regression, and logistic regression. Linear regression aims to minimize the mean squared error between predicted and actual values. Gradient descent is used to iteratively adjust parameters to reduce the cost function. Polynomial regression extends linear regression to handle nonlinear relationships. Logistic regression uses sigmoid functions to model probabilities for binary classification problems.
GUI based Face detection using Viola-Jones algorithm in MATLAB.Binita Khua
The document discusses the Viola-Jones face detection algorithm. It outlines how the algorithm uses Haar-like features and an integral image to quickly detect faces in images. The Viola-Jones algorithm searches an image with sliding boxes and detects Haar-like features within each box, such as edges, lines, and four-sided shapes. An integral image allows for fast calculation of Haar-like feature values by summing pixel values within regions. Together, Haar-like features and the integral image allow the Viola-Jones algorithm to efficiently detect the presence of faces in images.
Avihu Efrat's Viola and Jones face detection slideswolf
The document summarizes the Viola-Jones object detection framework. It uses a cascade of classifiers with increasingly more complex features trained with AdaBoost to rapidly detect objects. Integral images allow for very fast feature evaluations. The framework was applied to face detection, achieving very fast average detection speeds of 270 microseconds per sub-window while maintaining low false positive rates.
Rapid object detection using boosted cascade of simple featuresHirantha Pradeep
1. The document presents the seminal work of Viola and Jones on rapid object detection using boosted cascades of simple features.
2. It introduces integral images for fast feature evaluation and uses AdaBoost for feature selection and classifier training in a cascade structure.
3. The cascade approach combines classifiers such that earlier ones rapidly reject negatives while later ones focus on positives, achieving real-time detection rates.
1. The document presents the Viola-Jones object detection framework which uses integral images, AdaBoost learning, and a cascade classifier structure for real-time object detection, such as face detection.
2. It introduces integral images as an image representation for fast feature extraction, AdaBoost for selecting important features, and a cascade structure for increased detection speed while maintaining accuracy.
3. Experiments showed the cascade classifier approach was nearly 10 times faster than a single classifier while maintaining high detection rates, enabling real-time face detection.
1. The document summarizes the robust real-time face detection method proposed by Viola and Jones in 2002, which uses integral images for fast feature computation, AdaBoost for feature selection, and a cascade structure for real-time processing.
2. It describes how integral images allow computing rectangular features in constant time, and how AdaBoost selects the most discriminative features by iteratively assigning higher weights to misclassified examples.
3. Finally, it explains that the cascade structure filters out most negative sub-windows using simple classifiers at the top, focusing computation only on the few potentially positive windows.
Robust Real-time Face Detection by Paul Viola and Michael Jones addresses three challenges:
1. Rapidly computing features using an integral image representation
2. Selecting discriminative features using AdaBoost
3. Achieving real-time performance through a cascade of classifiers that filter non-face regions with simple classifiers before more complex analysis.
Gaze transformers use vision transformers for gaze estimation from facial images. A hybrid model combines a CNN for image features with a transformer. It outperforms pure transformer and CNN models. Ablation studies show removing self-attention or convolutional layers hurts performance. Pre-training on a large dataset helps transformers achieve state-of-the-art results, and future methods may rely more on pre-training for gaze estimation tasks.
hands on machine learning Chapter 4 model trainingJaey Jeong
This document summarizes key concepts from a chapter on model training in machine learning, including linear regression, gradient descent, polynomial regression, and logistic regression. Linear regression aims to minimize the mean squared error between predicted and actual values. Gradient descent is used to iteratively adjust parameters to reduce the cost function. Polynomial regression extends linear regression to handle nonlinear relationships. Logistic regression uses sigmoid functions to model probabilities for binary classification problems.
Unsupervised representation learning for gaze estimationJaey Jeong
This document summarizes a research paper on unsupervised representation learning for gaze estimation. The paper proposes an unsupervised learning framework that uses a large amount of unlabeled eye image data to learn a gaze representation. This representation is used to train a gaze redirection network and support few-shot gaze estimation with only a small number of labeled samples. The method learns the representation using a feature extractor network and differences in representations between aligned image pairs. Evaluation on three datasets shows the approach can accurately estimate gaze using as few as 10-100 labeled samples per person.
A Re-evaluation of Pedestrian Detection on Riemannian ManifoldsDiego Tosato
Boosting covariance data on Riemannian manifolds has proven to be a convenient strategy in a pedestrian detection context. In this paper we show that the detection performances of the state-of-the-art approach of Tuzel et al. can be greatly improved, from both a computational and a qualitative point of view, by considering practical and theoretical issues, and allowing also the estimation of occlusions in a fine way. The resulting detection system reaches the best performance on the INRIA dataset, setting novel state-of-theart results.
In machine learning, model selection is a bit more nuanced than simply picking the 'right' or 'wrong' algorithm. In practice, the workflow includes (1) selecting and/or engineering the smallest and most predictive feature set, (2) choosing a set of algorithms from a model family, and (3) tuning the algorithm hyperparameters to optimize performance. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search.
This talk presents a new open source Python library, Yellowbrick (scikit-yb.org), which extends the Scikit-Learn API with a visual transfomer (visualizer) that can incorporate visualizations of the model selection process into pipelines and modeling workflow. Visualizers enable machine learning practitioners to visually interpret the model selection process, steer workflows toward more predictive models, and avoid common pitfalls and traps. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow.
Improving accuracy of binary neural networks using unbalanced activation dist...Jaey Jeong
The document discusses improving the accuracy of binary neural networks (BNNs) through unbalanced activation distribution. It begins by explaining that BNNs currently suffer from accuracy degradation due to aggressive quantization. It then proposes shifting the threshold of the binary activation function to create an unbalanced distribution, similar to ReLU, which could improve accuracy. The paper describes experiments conducted on image classification tasks that demonstrate threshold shifting enhances BNN performance compared to the balanced distribution of previous BNN activation functions. It also analyzes the effects of additional activation functions and finds LeakyReLU works better than PReLU for further enhancing BNN accuracy.
Yellowbrick: Steering machine learning with visual transformersRebecca Bilbro
In machine learning, model selection is a bit more nuanced than simply picking the 'right' or 'wrong' algorithm. In practice, the workflow includes (1) selecting and/or engineering the smallest and most predictive feature set, (2) choosing a set of algorithms from a model family, and (3) tuning the algorithm hyperparameters to optimize performance. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search.
This talk presents a new Python library, Yellowbrick, which extends the Scikit-Learn API with a visual transfomer (visualizer) that can incorporate visualizations of the model selection process into pipelines and modeling workflow. Yellowbrick is an open source, pure Python project that extends Scikit-Learn with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib to create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow.
In this talk, we'll explore not only what you can do with Yellowbrick, but how it works under the hood (since we're always looking for new contributors!). We'll illustrate how Yellowbrick extends the Scikit-Learn and Matplotlib APIs with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the Scikit-Learn Pipeline process - providing iterative visual diagnostics throughout the transformation of high dimensional data.
Tablet gaze unconstrained appearance based gaze estimation in mobile tabletsJaey Jeong
The document presents the TabletGaze project which aims to perform gaze estimation on tablets without additional hardware through front-facing cameras. It collected an unconstrained dataset of 51 subjects with various postures and appearances. TabletGaze algorithms were developed using features like HoG and regression models like random forest. Evaluation showed mean errors of 3.17cm for person-independent gaze tracking in real-time on mobile, outperforming prior work. This allows for new applications involving hands-free interaction.
Object Tracking By Online Discriminative Feature Selection AlgorithmIRJET Journal
1) The document presents an online discriminative feature selection algorithm for object tracking. It aims to select discriminative features between the target object and background to improve tracking performance.
2) The algorithm formulates the feature selection problem as optimizing an objective function that maximizes the average confidence of positive samples while suppressing the average confidence of negative samples.
3) It uses a greedy sequential forward selection approach to select weak classifiers from a pool that maximize this objective function. This formulation directly couples the classifier score with sample importance, leading to a more robust and efficient tracker.
The document discusses machine learning techniques for processing sensor data from vehicles. It describes how machine learning can be used to create virtual sensors from raw data by analyzing features, selecting relevant data, preprocessing to remove noise, and building models. Examples are provided of using support vector machines and neural networks to classify yaw rate from sensor signals. The document also introduces a tool called Distortion that manages machine learning jobs by uploading data, running algorithms, and analyzing results.
Road signs detection using voila jone's algorithm with the help of opencvMohdSalim34
The document describes a project to detect German road signs using the Viola-Jones algorithm with OpenCV. The project team includes 5 members and is supervised by Farah Jamal Ansari. The objective is to train OpenCV to detect German road signs in images. OpenCV, Viola-Jones algorithm, and Python will be used to detect signs in 600 training images.
1) Randomized numerical linear algebra (RandNLA) algorithms can be used to solve large-scale least-squares problems by computing a randomized sketch of the design matrix in two steps and then obtaining approximate solutions.
2) The document implements and evaluates these RandNLA algorithms in Apache Spark on datasets up to terabytes in size, finding that Spark is well-suited due to the algorithms' parallelism and Spark's ability to cache data in memory.
3) The evaluation compares the performance of low-precision solvers that directly use the sketch and high-precision solvers that employ the sketch as a preconditioner, finding that both approaches can efficiently solve least-squares problems on large datasets.
Video Object Extraction Using Feature Matching Based on Nonlocal MattingMeidya Koeshardianto
1) Video object extraction involves extracting foreground and background objects from video sequences using matting equations and constraints like trimaps and scribbles.
2) Existing matting methods require constraints for each frame, but automatic constraints can be obtained through feature matching and nonlocal matting.
3) The presented method uses SIFT to detect keypoints for automatic scribbles, then performs nonlocal matting using Laplacian transforms on the graph to smoothly label pixels and extract video objects.
Automated Testing of Hybrid Simulink/Stateflow ControllersLionel Briand
This document discusses automated testing of Simulink/Stateflow controllers for automotive software. It presents an approach using black box search-based testing to generate test cases for closed-loop and open-loop controllers. The approach uses fitness functions to evaluate test results and provide failure explanation and detection. Case studies on industrial models demonstrate generating test inputs that reveal failures and visualizing the input space to explain under what conditions failures are likely to occur. The approach aims to help engineers test complex models without manual oracles or dealing with tool incompatibilities.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
Face recognition technology may help solve problems with identity verification by analyzing facial features instead of passwords or pins. The document outlines the key stages of face recognition systems including data acquisition, input processing, and image classification. It also discusses advantages like convenience and ease of use, as well as limitations such as an inability to distinguish identical twins. Potential applications are identified in government, security, and commercial sectors.
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.
This document discusses various camera shots and techniques including establishing shots to set the scene, wide shots and mid shots to show surroundings and character emotions, close ups to see details, point of view shots to see what a character sees, tracking shots to follow movement, and tilt shots for an artistic approach. These shots are used to convey information to an audience through visual storytelling.
This document appears to be the results of a survey about fashion magazine readership habits. It asks respondents questions about how often they read magazines, what elements attract them to buying a magazine, whether they would follow the magazine on social media, if freebies would make the magazine more interesting, what format they prefer to read in, how often the magazine should be published, how much they are willing to pay, what types of articles they want, if they would use a magazine website, and what title they prefer for a fashion magazine. The survey collects both multiple choice answers and open-ended responses to gain insights into readers' preferences.
Arkatay about Certificates for project managersJan Raaschou
The document provides an overview of project management certifications from several organizations such as PMI, IPMA, PRINCE2, and Scaled Agile. It discusses the different types of certifications available, their target experience levels, and benefits. The seminar agenda includes discussing which certifications are appropriate for individuals based on their experience and goals, as well as providing information on certification processes, requirements, and exams.
Unsupervised representation learning for gaze estimationJaey Jeong
This document summarizes a research paper on unsupervised representation learning for gaze estimation. The paper proposes an unsupervised learning framework that uses a large amount of unlabeled eye image data to learn a gaze representation. This representation is used to train a gaze redirection network and support few-shot gaze estimation with only a small number of labeled samples. The method learns the representation using a feature extractor network and differences in representations between aligned image pairs. Evaluation on three datasets shows the approach can accurately estimate gaze using as few as 10-100 labeled samples per person.
A Re-evaluation of Pedestrian Detection on Riemannian ManifoldsDiego Tosato
Boosting covariance data on Riemannian manifolds has proven to be a convenient strategy in a pedestrian detection context. In this paper we show that the detection performances of the state-of-the-art approach of Tuzel et al. can be greatly improved, from both a computational and a qualitative point of view, by considering practical and theoretical issues, and allowing also the estimation of occlusions in a fine way. The resulting detection system reaches the best performance on the INRIA dataset, setting novel state-of-theart results.
In machine learning, model selection is a bit more nuanced than simply picking the 'right' or 'wrong' algorithm. In practice, the workflow includes (1) selecting and/or engineering the smallest and most predictive feature set, (2) choosing a set of algorithms from a model family, and (3) tuning the algorithm hyperparameters to optimize performance. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search.
This talk presents a new open source Python library, Yellowbrick (scikit-yb.org), which extends the Scikit-Learn API with a visual transfomer (visualizer) that can incorporate visualizations of the model selection process into pipelines and modeling workflow. Visualizers enable machine learning practitioners to visually interpret the model selection process, steer workflows toward more predictive models, and avoid common pitfalls and traps. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow.
Improving accuracy of binary neural networks using unbalanced activation dist...Jaey Jeong
The document discusses improving the accuracy of binary neural networks (BNNs) through unbalanced activation distribution. It begins by explaining that BNNs currently suffer from accuracy degradation due to aggressive quantization. It then proposes shifting the threshold of the binary activation function to create an unbalanced distribution, similar to ReLU, which could improve accuracy. The paper describes experiments conducted on image classification tasks that demonstrate threshold shifting enhances BNN performance compared to the balanced distribution of previous BNN activation functions. It also analyzes the effects of additional activation functions and finds LeakyReLU works better than PReLU for further enhancing BNN accuracy.
Yellowbrick: Steering machine learning with visual transformersRebecca Bilbro
In machine learning, model selection is a bit more nuanced than simply picking the 'right' or 'wrong' algorithm. In practice, the workflow includes (1) selecting and/or engineering the smallest and most predictive feature set, (2) choosing a set of algorithms from a model family, and (3) tuning the algorithm hyperparameters to optimize performance. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search.
This talk presents a new Python library, Yellowbrick, which extends the Scikit-Learn API with a visual transfomer (visualizer) that can incorporate visualizations of the model selection process into pipelines and modeling workflow. Yellowbrick is an open source, pure Python project that extends Scikit-Learn with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib to create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow.
In this talk, we'll explore not only what you can do with Yellowbrick, but how it works under the hood (since we're always looking for new contributors!). We'll illustrate how Yellowbrick extends the Scikit-Learn and Matplotlib APIs with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the Scikit-Learn Pipeline process - providing iterative visual diagnostics throughout the transformation of high dimensional data.
Tablet gaze unconstrained appearance based gaze estimation in mobile tabletsJaey Jeong
The document presents the TabletGaze project which aims to perform gaze estimation on tablets without additional hardware through front-facing cameras. It collected an unconstrained dataset of 51 subjects with various postures and appearances. TabletGaze algorithms were developed using features like HoG and regression models like random forest. Evaluation showed mean errors of 3.17cm for person-independent gaze tracking in real-time on mobile, outperforming prior work. This allows for new applications involving hands-free interaction.
Object Tracking By Online Discriminative Feature Selection AlgorithmIRJET Journal
1) The document presents an online discriminative feature selection algorithm for object tracking. It aims to select discriminative features between the target object and background to improve tracking performance.
2) The algorithm formulates the feature selection problem as optimizing an objective function that maximizes the average confidence of positive samples while suppressing the average confidence of negative samples.
3) It uses a greedy sequential forward selection approach to select weak classifiers from a pool that maximize this objective function. This formulation directly couples the classifier score with sample importance, leading to a more robust and efficient tracker.
The document discusses machine learning techniques for processing sensor data from vehicles. It describes how machine learning can be used to create virtual sensors from raw data by analyzing features, selecting relevant data, preprocessing to remove noise, and building models. Examples are provided of using support vector machines and neural networks to classify yaw rate from sensor signals. The document also introduces a tool called Distortion that manages machine learning jobs by uploading data, running algorithms, and analyzing results.
Road signs detection using voila jone's algorithm with the help of opencvMohdSalim34
The document describes a project to detect German road signs using the Viola-Jones algorithm with OpenCV. The project team includes 5 members and is supervised by Farah Jamal Ansari. The objective is to train OpenCV to detect German road signs in images. OpenCV, Viola-Jones algorithm, and Python will be used to detect signs in 600 training images.
1) Randomized numerical linear algebra (RandNLA) algorithms can be used to solve large-scale least-squares problems by computing a randomized sketch of the design matrix in two steps and then obtaining approximate solutions.
2) The document implements and evaluates these RandNLA algorithms in Apache Spark on datasets up to terabytes in size, finding that Spark is well-suited due to the algorithms' parallelism and Spark's ability to cache data in memory.
3) The evaluation compares the performance of low-precision solvers that directly use the sketch and high-precision solvers that employ the sketch as a preconditioner, finding that both approaches can efficiently solve least-squares problems on large datasets.
Video Object Extraction Using Feature Matching Based on Nonlocal MattingMeidya Koeshardianto
1) Video object extraction involves extracting foreground and background objects from video sequences using matting equations and constraints like trimaps and scribbles.
2) Existing matting methods require constraints for each frame, but automatic constraints can be obtained through feature matching and nonlocal matting.
3) The presented method uses SIFT to detect keypoints for automatic scribbles, then performs nonlocal matting using Laplacian transforms on the graph to smoothly label pixels and extract video objects.
Automated Testing of Hybrid Simulink/Stateflow ControllersLionel Briand
This document discusses automated testing of Simulink/Stateflow controllers for automotive software. It presents an approach using black box search-based testing to generate test cases for closed-loop and open-loop controllers. The approach uses fitness functions to evaluate test results and provide failure explanation and detection. Case studies on industrial models demonstrate generating test inputs that reveal failures and visualizing the input space to explain under what conditions failures are likely to occur. The approach aims to help engineers test complex models without manual oracles or dealing with tool incompatibilities.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
Face recognition technology may help solve problems with identity verification by analyzing facial features instead of passwords or pins. The document outlines the key stages of face recognition systems including data acquisition, input processing, and image classification. It also discusses advantages like convenience and ease of use, as well as limitations such as an inability to distinguish identical twins. Potential applications are identified in government, security, and commercial sectors.
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.
This document discusses various camera shots and techniques including establishing shots to set the scene, wide shots and mid shots to show surroundings and character emotions, close ups to see details, point of view shots to see what a character sees, tracking shots to follow movement, and tilt shots for an artistic approach. These shots are used to convey information to an audience through visual storytelling.
This document appears to be the results of a survey about fashion magazine readership habits. It asks respondents questions about how often they read magazines, what elements attract them to buying a magazine, whether they would follow the magazine on social media, if freebies would make the magazine more interesting, what format they prefer to read in, how often the magazine should be published, how much they are willing to pay, what types of articles they want, if they would use a magazine website, and what title they prefer for a fashion magazine. The survey collects both multiple choice answers and open-ended responses to gain insights into readers' preferences.
Arkatay about Certificates for project managersJan Raaschou
The document provides an overview of project management certifications from several organizations such as PMI, IPMA, PRINCE2, and Scaled Agile. It discusses the different types of certifications available, their target experience levels, and benefits. The seminar agenda includes discussing which certifications are appropriate for individuals based on their experience and goals, as well as providing information on certification processes, requirements, and exams.
Проект "Біотех-реабілітація поранених" - інноваційний проект з відновлення пошкоджених кісток кінцівок за допомогою стовбурових клітин пацієнта (що дозволяє уникнути ампутації)
Este documento presenta una introducción a las curiosidades y secretos escondidos en varias películas de Disney como El aprendiz de brujo, Frozen, Up, Toy Story, El libro de la selva y El Rey León. Incluye secciones sobre personajes clave y detalles poco conocidos en las tramas de estas populares películas de animación de Disney.
تعریفی که برای بازاریابی محتوایی توسط موسسه ی بازاریابی محتوایی ارائه شده این است که:
“بازاریابی محتوایی نگرشی است; بر پایه ی ایجاد و رساندن محتوایی ارزشمند، مناسب و منطقی برای جذب و حفظ مشتری های مناسب که با ضوابط آشنا هستند”.
درحالیکه تعریف ارائه شده یک تعریف رک و راست است، تعریف شما از بازاریابی محتوایی میتواند منوط به نیاز های کاری شما متفاوت باشد. برای مثال، Pushing Social (یک سایت فعال در زمینه ی بازاریابی محتوایی) بازاریابی محتوایی را “داستان سرایی برای فروش” تعریف می کند.
هایدی کوهن
اما هایدی کوهن، کارشناس در زمینه ی بازاریابی محتوایی و وبلاگ نویسی، یک تعریف دقیق تر از بازاریابی محتوایی ارائه میکند:
https://copify.ir/blog/%D8%A8%D8%A7%D8%B2%D8%A7%D8%B1%DB%8C%D8%A7%D8%A8%DB%8C-%D9%85%D8%AD%D8%AA%D9%88%D8%A7%DB%8C%DB%8C-%DA%86%DB%8C%D8%B3%D8%AA%D8%9F/
Le 14 Mars 2017, EUROSTAT publiait dans un communiqué de presse relatif à la production industrielle dans la zone euro, une étude comparée de la production industrielle de Décembre 2016 à Janvier 2017.
This document discusses Major Depressive Disorder, including its symptoms, diagnosis criteria, risk factors, and treatment options. It defines depression as a mental health problem that disrupts mood and functioning. The diagnostic criteria for major depression include having at least 5 symptoms for at least 2 weeks, such as depressed mood, loss of interest, changes in appetite or sleep, fatigue, feelings of worthlessness, difficulty concentrating, and suicidal thoughts. Treatment involves psychotherapy, medication such as SSRIs, and referral to psychiatric services for severe or treatment-resistant cases.
The Viola-Jones object detection framework uses Haar-like features, integral images, Adaboost, and cascading classifiers to provide competitive object detection rates in real-time. It was proposed in 2001 primarily for face detection. Haar-like features are similar to convolution kernels used to detect patterns in images. An integral image allows feature values to be calculated rapidly. Adaboost selects the best features from over 160,000 candidates. Cascading classifiers discard non-face windows quickly through multiple classifier stages to focus computation on probable faces. The algorithm was demonstrated in Python using OpenCV.
This document presents a literature review and proposed work plan for face recognition using a back propagation neural network. It summarizes the Viola-Jones face detection algorithm which uses Haar features and an integral image for real-time detection. The algorithm has high detection rates with low false positives. Future work will apply back propagation neural networks to extract features and recognize faces from a database of facial images in order to build a facial recognition system.
Road signs detection using voila jone's algorithm with the help of opencvMohdSalim34
This document provides an introduction and overview of a project to develop an automatic road sign detection system using the Viola-Jones object detection framework. It discusses the motivation for the project to address safety concerns from drivers missing road signs. The document outlines the contributions of the project, which are to train a classifier using OpenCV to detect German road signs in images by implementing the Viola-Jones algorithm. It also provides details on the Viola-Jones algorithm, which combines Haar features, integral images, AdaBoost testing, and cascading classifiers to rapidly detect objects in real-time.
This document summarizes a student project on face detection and recognition. The project used OpenCV with Python to detect faces in images and video in real-time. It extracts Haar features and compares them to a training database to recognize faces. The system was able to identify multiple faces with reasonable accuracy, though performance decreased with head tilts or low image quality. Future work could improve robustness to disguises and add emotion or gender analysis.
This document summarizes a student project on implementing object detection using the Viola-Jones technique. The technique uses Haar feature extraction and an AdaBoost classifier cascade to quickly and accurately detect objects like faces in images. The student developed implementations in Matlab and C++ to train classifiers and detect faces. The Viola-Jones technique was groundbreaking for providing real-time object detection with high accuracy rates compared to previous methods.
Detection and recognition of face using neural networkSmriti Tikoo
This document describes research on face detection and recognition using neural networks. It discusses using the Viola-Jones algorithm for face detection and a backpropagation neural network for face recognition. The Viola-Jones algorithm uses haar features, integral images, AdaBoost training, and cascading classifiers for real-time face detection. A backpropagation network with sigmoid activation functions is trained on facial images for recognition. Results show the network can accurately recognize faces after training. The document concludes the approach allows face recognition from an input image and discusses limitations and potential improvements.
This document discusses real-time image processing. It begins with an introduction and definitions of real-time and non-real-time processing. It then discusses the requirements for a real-time image processing platform, including high resolution/frame rate video input and low latency. The document outlines some advantages of real-time image processing such as immediate results and automation. It then provides an overview of an object detection system using Viola-Jones detection with integral images, AdaBoost learning, and a cascade classifier structure. Experimental results show the cascade classifier can detect faces in real-time.
Presented by Mr. Dinesh KS
Software Developer, Livares Technologies
Introduction
Object detection is a computer technology related to computer vision and image processing that
deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or
cars) in digital images and videos.
Face detection is a computer technology being used in a variety of applications that identifies
human faces in digital images.
The document summarizes an OpenCV based image processing attendance system. It discusses using OpenCV to detect faces in images and recognize faces by comparing features to a database. The key steps are face detection using Viola-Jones detection, face recognition using eigenfaces generated by principal component analysis to project faces into "face space", and measuring similarity by distance between projections.
This document describes a project to implement real-time facial recognition using OpenCV and Python. The project uses a laptop's webcam to capture video frames and detect and recognize faces in each frame. It trains an image dataset with face images and IDs then detects faces in each new video frame. It predicts faces by comparing features to the training data and labels matches based on a confidence level threshold. The document outlines the use of Haar cascade classifiers, LBPH algorithms, and OpenCV functions to complete the facial recognition process in real-time on new video frames from the webcam.
This document describes a project to implement real-time facial recognition using OpenCV and Python. The project uses a laptop's webcam to capture video frames and detect and recognize faces in each frame. It trains an image dataset with face images and IDs then detects faces in each new video frame. It predicts faces by comparing features to the training data and labels matches based on a confidence level threshold. The document outlines the use of Haar cascade classifiers, LBPH algorithms, and OpenCV functions to complete the facial recognition process in real-time on new video frames from the webcam.
IRJET- Face Detection and Tracking Algorithm using Open CV with Raspberry PiIRJET Journal
This document describes a face detection and tracking algorithm using OpenCV with the Raspberry Pi. It discusses using the Haar cascade algorithm for face detection and tracking in real-time video streams from a Pi camera connected to a Raspberry Pi. The algorithm works in two modules - face detection using Haar features and integral images to quickly detect faces, followed by face tracking across subsequent video frames. The algorithm is tested on a Raspberry Pi to enable real-time face detection and tracking applications like security systems.
AI driven classification framework for advanced Test AutomationSTePINForum
by Shubhradeep Nandi, Head of Digital, MSys Technologies at STeP-IN SUMMIT 2018 15th International Conference on Software Testing on August 31, 2018 at Taj, MG Road, Bengaluru
The model explains how we can Automate System using Artificial Intelligence.
It broadly concerns about:-
1. Lane Detection.
2. Traffic Sign Classification.
3. Behavioural Cloning.
Face Detection System on Ada boost Algorithm Using Haar ClassifiersIJMER
This paper presents a hardware architecture for real-time face detection using AdaBoost algorithm and Haar features. The architecture generates integral images and classifies sub-windows using optimized parallel processing. It was designed with Verilog HDL and implemented on an FPGA. The performance was measured and showed a 35x increase in speed over software implementation on a general processor. Key aspects of the architecture include optimized generation of integral images, parallel classification of multiple Haar classifiers, and scalability to configurable devices.
This document describes a hybrid face detection system that combines the Viola-Jones method and skin detection. It first discusses the Viola-Jones method, including integral images, AdaBoost, and cascade classifiers. It then discusses skin detection techniques including building a skin model using color spaces like HSV and YCbCr, skin segmentation, and morphological operations. Finally, it proposes a framework that uses Viola-Jones to initially detect upper bodies, then applies skin detection techniques within those regions to more accurately detect faces.
Andrii Belas "Overview of object detection approaches: cases, algorithms and...Lviv Startup Club
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This document discusses deep learning techniques for object detection and recognition. It provides an overview of computer vision tasks like image classification and object detection. It then discusses how crowdsourcing large datasets from the internet and advances in machine learning, specifically deep convolutional neural networks (CNNs), have led to major breakthroughs in object detection. Several state-of-the-art CNN models for object detection are described, including R-CNN, Fast R-CNN, Faster R-CNN, SSD, and YOLO. The document also provides examples of applying these techniques to tasks like face detection and detecting manta rays from aerial videos.
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Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
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Chapter 4
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Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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2. Real Time Face Detection
Systems Using Viola Johns
Algorithm
-Sreerag Sreenath
Sec A
Final Year B.Tech ECE
January 24 , 2017
3. Face Detection
3
Basic idea: slide a window across image and evaluate a face model at every
location of a given image
4. Overview
• Robust – very high Detection Rate (True-Positive Rate) & very low False-
Positive Rate… always.
• Real Time – For practical applications at least 2 frames per second must be
processed.
• Face Detection – not recognition. The goal is to distinguish faces from non-
faces (face detection is the first step in the identification process)
4
6. Problems…..?
• How to define a feature?
-- Edge Detection to Haar features
• How to calculate area fast?
-- Integral Image
6
7. Steps in Voila Jones Face Detection
Algorithm
Haars
Features
Integral
Image
Adaboost Cascading
7
8. Basic Introduction to edge detection
Output image(right) has high intensity at pixels where the convolution kernel
pixel pattern matched perfectly with the input image
8
9. Haar Features
9
• Haar features are similar to these convolution kernels which are used to
detect the presence of that feature in the given image.
• Each feature results in a single value which is calculated by subtracting the
sum of pixels under white rectangle from the sum of pixels under black
10. Haar Features
10
• Viola Jones algorithm uses a 24x24 window as the base window size to
start evaluating these features in any given image.
• If we consider all the possible parameters of the haars features like position,
scale and type we will end up calculating about 160,000+ features in this
window.
11. Integral Window
11
• In an integral image the value at pixel(x,y) is the sum of pixels above and to
the left of (x,y)
• Integral image allows for the calculation of sum of all pixels inside any given
rectangle using only four values at the Conner of the rectangle.
12. Adaboost
12
• As stated previously there can be 160,000+ feature values within a detector
at 24x24 base resolution which needs to be calculated. But it is to be
understood that only few set of features will be useful among all these
features to identify a face .
13. Adaboost
13
• As Adaboost is a machine learning algorithm which helps in finding the best
among all these 160,000+ features. After these features are found, a
weighted combination of all these features in used in evaluating and
deciding any given window or not. Their accuracy can also be low more than
50 percent (better than random guessing)
• These best features are called as weak classifiers. Adaboost constructs a
strong classifier as a linear combination of these weak classifiers
14. Cascading
14
• The basic principle of the Viola-Jones face detection algorithm is to scan the
detector many times through the same image – each time with a new size.
• Even if an image should contain one or more faces it is obvious that an
excessive large amount of the evaluated sub-window would still be
negatives (non-faces).
• So the algorithm should concentrate on discarding non-faces quickly and
spend more time on the probable face regions
• Hence a single strong classifier formed out of linear combination of all best
features is not a good to evaluate on each window because of
computational cost
15. Cascading
15
• Therefore a cascade classifiers is used which is composed of stages each
containing a strong classifiers. So all the features are grouped into several
stages where each stage has certain number of features.
• The job of each stage is used to determine wheatear a given sub-window is
defiantly not a face or may be a face. A given sub window is immediately
discarded as not a face if it fails in any of the stage
21. Further Improved Algorithms
21
• There have been a lot of research and development in object detection
algorithms since then. Feature Point detection, Bag-of-Words
models, Histogram-of-oriented gradients (HOG), Deformable Parts
Models, Exemplar models, etc. are some modern techniques that have been
used to great success. Deep learning (convolutional neural networks) is the
absolute state-of-the-art technique for object detection and has produced
great strides in the field.
• There are a lot of great papers and cutting-edge research. For example,
consider Facebook's DeepFace face recognition algorithm, DeepFace had
produced a face recognition accuracy of 97.35% on the Labelled Faces in
the Wild dataset, which is quite near human-level performance (97.53%)! So
yes, modern and state-of-the-art algorithms hold a lot of promise.
Have you guys every used any of these image filters before? Maybe snapchat, maybe facebook messenger? And many moreBut have you ever wondered how they work that too so fast and efficient?
Good evening Sir and my dear friends Today I will be taking the seminar of the topic Real time Face detection systems, My name is Sreerag Sreenath, Final year Btech bearing the roll number 134153
So first lets get to the basics.