Название: Advanced Image Processing with Python and OpenCV: Implementing High-Performance Computer Vision Solutions for Object Detection, Image Recognition, and Augmented Reality Applications Автор: Greyson Chesterfield Издательство: Donbri Publishers Год: 2024 Страниц: 159 Язык: английский Формат: pdf, azw3, epub, mobi Размер: 10.1 MB
Unlock the full potential of Python and OpenCV with Advanced Image Processing with Python and OpenCV. This comprehensive guide delves into high-performance computer vision techniques that power today's cutting-edge technologies, including object detection, image recognition, and augmented reality. Designed for both beginners and seasoned developers, this book provides step-by-step guidance through complex topics, from setting up your environment to advanced image processing techniques.
Whether you're creating robust applications for healthcare, autonomous vehicles, or dynamic media, you’ll find the techniques and code examples to bring your projects to life. With clear explanations and hands-on exercises, you'll gain practical skills in image filtering, geometric transformations, feature extraction, deep learning-based segmentation, and much more. Equip yourself with the knowledge to solve real-world challenges and advance in the rapidly growing field of computer vision.
Computer vision is a subfield of Artificial Intelligence (AI) that focuses on enabling machines to interpret and understand visual information from the world. It aims to replicate human vision capabilities, allowing computers to process and analyze images or video streams to derive meaningful information. The significance of computer vision is highlighted by its ability to automate tasks that require visual understanding.
Some common applications include: Facial Recognition: This technology is widely used in security systems and social media platforms. It involves identifying and verifying individuals based on facial features. Object Detection: Identifying and locating objects within an image or video stream is crucial for applications like self-driving cars, robotics, and surveillance. Medical Image Analysis: Computer vision plays a vital role in analyzing medical images, such as X-rays, MRIs, and CT scans, to assist healthcare professionals in diagnosing diseases. Autonomous Vehicles: Self-driving cars rely on computer vision to navigate, recognize road signs, and detect pedestrians and obstacles. Augmented Reality: Computer vision is fundamental to AR applications, where digital content is superimposed onto the real world, requiring real-time understanding of the environment.
Computer vision combines elements from various disciplines, including mathematics, Computer Science, and cognitive science. The field has seen rapid advancements due to the development of Deep Learning techniques, which have significantly improved the performance of computer vision tasks. Deep Learning algorithms, particularly convolutional neural networks (CNNs), have become the standard for image classification and object detection.
OpenCV (Open Source Computer Vision Library) is one of the most popular libraries for image processing and computer vision. Initially developed by Intel, it has become a widely adopted tool among researchers, developers, and hobbyists. OpenCV provides a comprehensive suite of tools and functions for image manipulation, feature extraction, object detection, and more.
Python, a versatile and user-friendly programming language, has gained immense popularity in the field of computer vision due to its simplicity and readability. The combination of OpenCV and Python allows developers to implement complex image processing tasks with minimal code, making it an ideal choice for both beginners and experienced practitioners.
Start your journey today and transform the way machines see the world!
Contents:
Chapter 1: Introduction to Image Processing and Computer Vision Chapter 2: Setting Up Your Environment Chapter 3: Image Basics and Fundamentals Chapter 4: Image Filtering and Enhancement Chapter 5: Geometric Transformations Chapter 6: Image Segmentation Techniques Chapter 7: Feature Detection and Description Chapter 8: Object Detection and Recognition Chapter 9: Image Segmentation Techniques Chapter 10: Feature Extraction and Representation Chapter 11: Image Classification Techniques Chapter 12: Object Detection Techniques Chapter 13: Image Segmentation Techniques Chapter 14: Image Recognition and Classification Chapter 15: Object Detection Techniques in Computer Vision Chapter 16: Image Segmentation Techniques in Computer Vision Chapter 17: Future Trends in Image Processing and Computer Vision
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