Denmark and Switzerland are small and successful countries with exceptionally content populations. However, they have very different political institutions and economic models. They have followed the general tendency in the West toward economic convergence, but both countries have managed to stay on top. They both have a strong liberal tradition, but otherwise their economic strategies are a welfare state model for Denmark and a safe haven model for Switzerland. The Danish welfare state is tax-based, while the expenditures for social welfare are insurance-based in Switzerland. The political institutions are a multiparty unicameral system in Denmark, and a permanent coalition system with many referenda and strong local government in Switzerland. Both approaches have managed to ensure smoothly working political power-sharing and economic systems that allocate resources in a fairly efficient way. To date, they have also managed to adapt the economies to changes in the external environment with a combination of stability and flexibility.

Denmark and Switzerland are small and successful countries with exceptionally content populations. However, they have very different political institutions and economic models. They have followed the general tendency in the West toward economic convergence, but both countries have managed to stay on top. They both have a strong liberal tradition, but otherwise their economic strategies are a welfare state model for Denmark and a safe haven model for Switzerland. The Danish welfare state is tax-based, while the expenditures for social welfare are insurance-based in Switzerland. The political institutions are a multiparty unicameral system in Denmark, and a permanent coalition system with many referenda and strong local government in Switzerland. Both approaches have managed to ensure smoothly working political power-sharing and economic systems that allocate resources in a fairly efficient way. To date, they have also managed to adapt the economies to changes in the external environment with a combination of stability and flexibility.

Get to grips with traditional computer vision algorithms and deep learning approaches, and build real-world applications with OpenCV and other machine learning frameworks Key Features Understand how to capture high-quality image data, detect and track objects, and process the actions of animals or humans Implement your learning in different areas of computer vision Explore advanced concepts in OpenCV such as machine learning, artificial neural network, and augmented reality Book DescriptionOpenCV is a native cross-platform C++ library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. This book will get you hands-on with a wide range of intermediate to advanced projects using the latest version of the framework and language, OpenCV 4 and Python 3.8, instead of only covering the core concepts of OpenCV in theoretical lessons. This updated second edition will guide you through working on independent hands-on projects that focus on essential OpenCV concepts such as image processing, object detection, image manipulation, object tracking, and 3D scene reconstruction, in addition to statistical learning and neural networks. You'll begin with concepts such as image filters, Kinect depth sensor, and feature matching. As you advance, you'll not only get hands-on with reconstructing and visualizing a scene in 3D but also learn to track visually salient objects. The book will help you further build on your skills by demonstrating how to recognize traffic signs and emotions on faces. Later, you'll understand how to align images, and detect and track objects using neural networks. By the end of this OpenCV Python book, you'll have gained hands-on experience and become proficient at developing advanced computer vision apps according to specific business needs. What you will learn Generate real-time visual effects using filters and image manipulation techniques such as dodging and burning Recognize hand gestures in real-time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor Learn feature extraction and feature matching to track arbitrary objects of interest Reconstruct a 3D real-world scene using 2D camera motion and camera reprojection techniques Detect faces using a cascade classifier and identify emotions in human faces using multilayer perceptrons Classify, localize, and detect objects with deep neural networks Who this book is forThis book is for intermediate-level OpenCV users who are looking to enhance their skills by developing advanced applications. Familiarity with OpenCV concepts and Python libraries, and basic knowledge of the Python programming language are assumed.

A practical guide to understanding the core machine learning and deep learning algorithms, and implementing them to create intelligent image processing systems using OpenCV 4 Key Features Gain insights into machine learning algorithms, and implement them using OpenCV 4 and scikit-learn Get up to speed with Intel OpenVINO and its integration with OpenCV 4 Implement high-performance machine learning models with helpful tips and best practices Book DescriptionOpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition. You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you'll get to grips with the latest Intel OpenVINO for building an image processing system. By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4. What you will learn Understand the core machine learning concepts for image processing Explore the theory behind machine learning and deep learning algorithm design Discover effective techniques to train your deep learning models Evaluate machine learning models to improve the performance of your models Integrate algorithms such as support vector machines and Bayes classifier in your computer vision applications Use OpenVINO with OpenCV 4 to speed up model inference Who this book is forThis book is for Computer Vision professionals, machine learning developers, or anyone who wants to learn machine learning algorithms and implement them using OpenCV 4. If you want to build real-world Computer Vision and image processing applications powered by machine learning, then this book is for you. Working knowledge of Python programming is required to get the most out of this book.

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book * Load, store, edit, and visualize data using OpenCV and Python * Grasp the fundamental concepts of classification, regression, and clustering * Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide * Evaluate, compare, and choose the right algorithm for any task Who This Book Is For This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks. What You Will Learn * Explore and make effective use of OpenCV's machine learning module * Learn deep learning for computer vision with Python * Master linear regression and regularization techniques * Classify objects such as flower species, handwritten digits, and pedestrians * Explore the effective use of support vector machines, boosted decision trees, and random forests * Get acquainted with neural networks and Deep Learning to address real-world problems * Discover hidden structures in your data using k-means clustering * Get to grips with data pre-processing and feature engineering In Detail Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google's DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch! Style and approach OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.

Get savvy with OpenCV and actualize cool computer vision applications About This Book * Use OpenCV's Python bindings to capture video, manipulate images, and track objects * Learn about the different functions of OpenCV and their actual implementations. * Develop a series of intermediate to advanced projects using OpenCV and Python Who This Book Is For This learning path is for someone who has a working knowledge of Python and wants to try out OpenCV. This Learning Path will take you from a beginner to an expert in computer vision applications using OpenCV. OpenCV's application are humongous and this Learning Path is the best resource to get yourself acquainted thoroughly with OpenCV. What You Will Learn * Install OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect - all on Windows, Mac or Ubuntu * Apply "curves" and other color transformations to simulate the look of old photos, movies, or video games * Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image * Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor * Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques * Detect and recognize street signs using a cascade classifier and support vector machines (SVMs) * Identify emotional expressions in human faces using convolutional neural networks (CNNs) and SVMs * Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features In Detail OpenCV is a state-of-art computer vision library that allows a great variety of image and video processing operations. OpenCV for Python enables us to run computer vision algorithms in real time. This learning path proposes to teach the following topics. First, we will learn how to get started with OpenCV and OpenCV3's Python API, and develop a computer vision application that tracks body parts. Then, we will build amazing intermediate-level computer vision applications such as making an object disappear from an image, identifying different shapes, reconstructing a 3D map from images , and building an augmented reality application, Finally, we'll move to more advanced projects such as hand gesture recognition, tracking visually salient objects, as well as recognizing traffic signs and emotions on faces using support vector machines and multi-layer perceptrons respectively. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: * OpenCV Computer Vision with Python by Joseph Howse * OpenCV with Python By Example by Prateek Joshi * OpenCV with Python Blueprints by Michael Beyeler Style and approach This course aims to create a smooth learning path that will teach you how to get started with will learn how to get started with OpenCV and OpenCV 3's Python API, and develop superb computer vision applications. Through this comprehensive course, you'll learn to create computer vision applications from scratch to finish and more!.

Design and develop advanced computer vision projects using OpenCV with Python About This Book * Program advanced computer vision applications in Python using different features of the OpenCV library * Practical end-to-end project covering an important computer vision problem * All projects in the book include a step-by-step guide to create computer vision applications Who This Book Is For This book is for intermediate users of OpenCV who aim to master their skills by developing advanced practical applications. Readers are expected to be familiar with OpenCV's concepts and Python libraries. Basic knowledge of Python programming is expected and assumed. What You Will Learn * Generate real-time visual effects using different filters and image manipulation techniques such as dodging and burning * Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor * Learn feature extraction and feature matching for tracking arbitrary objects of interest * Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques * Track visually salient objects by searching for and focusing on important regions of an image * Detect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer peceptrons (MLPs) * Recognize street signs using a multi-class adaptation of support vector machines (SVMs) * Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features In Detail OpenCV is a native cross platform C++ Library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android. Developers using OpenCV build applications to process visual data; this can include live streaming data from a device like a camera, such as photographs or videos. OpenCV offers extensive libraries with over 500 functions This book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this book teach the reader how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization. By the end of this book, readers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications. Style and approach This book covers independent hands-on projects that teach important computer vision concepts like image processing and machine learning for OpenCV with multiple examples.