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Learn how to build your own computer vision (CV) applications quickly and easily with SimpleCV, an open source framework written in Python. Through examples of real-world applications, this hands-on guide introduces you to basic CV techniques for collecting, processing, and analyzing streaming digital images. You’ll then learn how to apply these methods with SimpleCV, using sample Python code. All you need to get started is a Windows, Mac, or Linux system, and a willingness to put CV to work in a variety of ways. Programming experience is optional. Capture images from several sources, including webcams, smartphones, and Kinect Filter image input so your application processes only necessary information Manipulate images by performing basic arithmetic on pixel values Use feature detection techniques to focus on interesting parts of an image Work with several features in a single image, using the NumPy and SciPy Python libraries Learn about optical flow to identify objects that change between two image frames Use SimpleCV’s command line and code editor to run examples and test techniques
This pioneering text/reference presents a detailed focus on the use of machine vision techniques in industrial inspection applications. An internationally renowned selection of experts provide insights on a range of inspection tasks, drawn from their cutting-edge work in academia and industry, covering practical issues of vision system integration for real-world applications. Topics and features: presents a comprehensive review of state-of-the-art hardware and software tools for machine vision, and the evolution of algorithms for industrial inspection; includes in-depth descriptions of advanced inspection methodologies and machine vision technologies for specific needs; discusses the latest developments and future trends in imaging and vision techniques for industrial inspection tasks; provides a focus on imaging and vision system integration, implementation, and optimization; describes the pitfalls and barriers to developing successful inspection systems for smooth and efficient manufacturing process.
Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.
Step-by-step tutorials on generative adversarial networks in python for image synthesis and image translation.
"Learning the fundamentals of image processing puts a powerful and very useful tool at your fingertips. Learning computer vision in LabVIEW is easy to learn, has excellent documentation, and is the base for prototyping all types of vision-based algorithms. Jobs in image processing are plentiful, and being able to learn computer and machine vision will give you a strong background to more easily pick up other computer vision tools such as OpenCV, Matlab, SimpleCV and so on. Suitable for beginning programmers, through this course of 26 lectures and over 4 hours of content, you'll learn all about computer vision and establish a strong understanding of the concept behind image processing algorithms. Each chapter closes with exercises in which you will develop your own vision-based apps, putting your new learned skills into practical use immediately. Starting with the installation of the LabVIEW Vision Development Toolkit, this course will take you through the main and fundamental image processing tools used in industry and research."--Resource description page.
Publishes papers reporting on research and development in optical science and engineering and the practical applications of known optical science, engineering, and technology.

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