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Learn techniques used in robot navigation, medical image analysis, and other computer vision applications
Item #:
17978674
Programming Computer Vision with Python: Tools and algorithms for analyzing images
90% of respondents would recommend this to a friend
Item #:
17978674
R$ 355
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What Stands Out
Comprehensive Guide
This book provides an in-depth exploration of computer vision techniques, making it ideal for both beginners and experienced programmers looking to enhance their skills with practical algorithms and tools.
Hands-On Examples
Packed with practical coding examples, it allows readers to implement solutions in real-time, which helps in understanding complex concepts through hands-on practice.
Python-Focused
Specifically tailored for Python programming, this book leverages the language's strengths, offering efficient tools and libraries that simplify the development of advanced image analysis applications.
Product Details
- Hands-on introduction to computer vision theory and algorithms
- Clear examples and complete code samples written in Python
- Ideal for students, researchers, and enthusiasts with basic programming skills
- Techniques for object recognition, 3D reconstruction, stereo imaging, and augmented reality
- Exercises to apply learned concepts and build efficient image retrieval techniques
- Access the OpenCV library through a Python interface for practical implementation
| Publisher | O'Reilly Media |
| Publication date | July 24, 2012 |
| Edition | 1st |
| Language | English |
| Print length | 260 pages |
| ISBN-10 | 1449316549 |
| ISBN-13 | 978-1449316549 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 7 x 0.68 x 9.19 inches (17.8 x 1.7 x 23.3 cm) |
Who Should Buy?
-
Aspiring Developers
Ideal for beginners aiming to build foundational skills in computer vision programming using Python.
-
Data Scientists
Perfect for professionals looking to implement image analysis techniques in data science projects and research.
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Academics and Students
Suitable for students studying computer vision or related fields who require practical tools and algorithms.
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Non-technical Users
Inadequate for individuals without a programming background or interest in technical aspects of image processing.
Product Description
Programming Computer Vision with Python: Tools and algorithms for analyzing images
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Customer Questions & Answers
-
Question:
What is 'Programming Computer Vision with Python' about?
Answer: This book delves into the realm of computer vision using Python, highlighting various tools and algorithms for image analysis. It provides readers with foundational concepts and practical applications, empowering them to manage and interpret visual data effectively. You'll learn about techniques such as feature detection, image manipulation, and machine learning integration. It's particularly useful for developers, researchers, or hobbyists looking to gain hands-on experience with computer vision projects. -
Question:
Who is the target audience for this book?
Answer: The book is primarily aimed at software developers, data scientists, and computer science students with an interest in image processing, computer vision, and artificial intelligence. It caters to those who possess a basic understanding of Python programming. Whether you are looking to enhance your skill set for professional development or personal projects, this book is structured to accommodate both novice and intermediate learners. -
Question:
What programming language does this book focus on?
Answer: The focus of this book is on Python, one of the most popular programming languages in the fields of data science and machine learning. Python's extensive libraries such as OpenCV and scikit-image are featured prominently, allowing readers to implement various image processing techniques effortlessly. Its readable syntax makes it an excellent choice for both beginners and experienced programmers looking to explore advanced computer vision topics. -
Question:
Are there practical examples in the book?
Answer: Yes, the book is rich in practical examples, including code snippets and real-world applications. Each chapter contains hands-on projects designed to reinforce the concepts presented. For instance, readers can experiment with image filtering, object recognition, and even developing applications for automatic image tagging. These examples enable readers to apply the theoretical knowledge gained to practical scenarios, enhancing their learning experience. -
Question:
What topics are covered in the book?
Answer: Key topics in 'Programming Computer Vision with Python' include foundational image processing techniques, advanced image manipulation, web scraping for image data, and machine learning integration for enhanced image analysis. The book also discusses the latest algorithms and tools used in the industry, equipping readers with the knowledge to tackle cutting-edge computer vision challenges across various domains like retail, healthcare, and autonomous vehicles. -
Question:
How does this book compare to other computer vision resources?
Answer: This book offers a practical, hands-on approach compared to many other computer vision resources that may focus solely on theory. By using Python's libraries, it bridges the gap between conceptual understanding and real-world application. Many readers appreciate the straightforward explanations and the sequential buildup of complexity, making it a preferred choice for those who value a structured learning path and versatile coding exercises. -
Question:
Do I need any prior experience with computer vision to read this book?
Answer: While prior experience in computer vision is beneficial, it is not a prerequisite. The book begins with fundamental concepts, gradually allowing readers to build their understanding. Basic knowledge of Python programming is assumed, and the author’s clear explanations help demystify complex topics. This approach makes it feasible for readers without extensive backgrounds in computer vision to engage with the material and develop their skills effectively. -
Question:
Is this book suitable for self-study?
Answer: Yes, 'Programming Computer Vision with Python' is well-suited for self-study, thanks to its structured layout and practical project-based approach. Each chapter is designed to stand alone, allowing readers to progress at their own pace. The inclusion of exercises and projects reinforces learning and promotes the application of concepts. Whether you study independently or as part of a learning group, you'll find the content approachable and engaging. -
Question:
What resources are included with the book?
Answer: The book includes various resources, such as code examples, supplementary materials, and references to online platforms where you can find additional datasets and tools. These resources enhance the learning experience by providing practical tools you can use alongside the book. Additionally, the author's insights and tips are invaluable for navigating more advanced concepts, resulting in a comprehensive educational package for aspiring computer vision practitioners. -
Question:
Where can I buy 'Programming Computer Vision with Python: Tools and algorithms for analyzing images 1st Edition'?
Answer: You can purchase 'Programming Computer Vision with Python: Tools and algorithms for analyzing images 1st Edition' from Ubuy. Ubuy offers a convenient platform to shop for a wide range of books and educational materials, making it easy to find and acquire the resources you need for your computer vision journey.
Computer Vision & Pattern Recognition Editorial Review
**** "Programming Computer Vision with Python" stands out as an informative and compact resource for anyone eager to delve into the world of image processing and computer vision through Python. Many readers appreciate the clarity with which complex tools and algorithms are explained, making advanced topics more accessible, especially for newcomers to the field. The book is particularly praised for its practical approach, empowering users to engage with coding examples and try them out directly. However, the journey is not without challenges. A common concern among users is that while the book is rich in content, it can sometimes feel overwhelming, with some readers needing to spend several days on just a single page to grasp concepts and seek further clarification through external resources. This depth may deter those who are unprepared for a steep learning curve or who seek lightweight references. A notable drawback mentioned is the absence of downloadable code samples or accompanying materials, as readers are required to manually type out code snippets that can range from 10 to 50 lines. This may prove tedious, especially for beginners. Furthermore, the book's illustrations and examples, which are vital in a visually driven topic, suffer due to black-and-white printing and some copies being smudged or poorly printed. Additionally, the text does not address some modern developments in the field, such as the current status of the Python Imaging Library (PIL) and its replacement by the more updated PILLOW, which can leave some users frustrated. Though the book has its merits and is hailed as a solid guide for aspiring programmers in computer vision, it also has elements that could be disorienting for total beginners. **
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Pros
- Well-written and clear explanations of complex concepts
- Practical approach with engaging coding examples
- Comprehensive introduction to image processing and computer vision
- Good balance of theory and practice
Cons
- Steep learning curve and overwhelming content for beginners
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R$ 355
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Features & Benefits
- Gain a basic understanding of computer vision's theory and algorithms
- Follow clear examples written in Python
- Get complete code samples and explanations
- Apply what you've learned with exercises
- Ideal for students, researchers, and enthusiasts with basic programming and mathematical skills
- Learn techniques used in robot navigation, medical image analysis, and other computer vision applications
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