Alexey Spizhevoy - OpenCV 3 Computer Vision with Python Cookbook

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Alexey Spizhevoy has over 7 years of experience in computer vision R&D. He has worked for 5 years at Itseez, the main OpenCV contributor, before it was acquired by Intel. He has contributed to video stabilization and photo stitching modules into OpenCV library. He has successfully participated in numerous Computer Vision projects in such areas as 3D reconstruction, video conferencing, object detection and tracking, semantic segmentation, driving assistance, and others. He holds a master's degree in computer science, and he is currently pursuing PhD.

Aleksandr Rybnikov has over 5 years of experience in C++ programming, including 3 years in the Computer Vision (CV) domain. He worked at Itseez, a company that supported and developed OpenCV, and then at Intel. He enriched OpenCV's dnn module by adding support of another two Deep Learning (DL) frameworks and many features, along with improving the existing functionality. As an engineer, he participated in CV and DL projects such as iris recognition, object detection, semantic segmentation, 6-DOF pose estimation, and digital hologram reconstruction. He has a master's degree in physics.

Joseph Howse lives in a Canadian fishing village with four cats; the cats like fish, but they prefer chicken.

Joseph provides computer vision expertise through his company, Nummist Media. He is a Packt author whose books include OpenCV for Secret Agents, OpenCV 3 Blueprints, Android Application Programming with OpenCV 3, iOS Application Development with OpenCV 3, Learning OpenCV 3 Computer Vision with Python, and Python Game Programming by Example.

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Computer Vision is a broad topic comprising a lot of different areas. If you want to start using Computer Vision algorithms in your projects, it may be ambiguous where the entry point is. Even if you're an experienced Computer Vision engineer, undoubtedly there are some technologies that you would want to explore in depth or get familiar with. In both cases, a practical approach works best. Only through applying methods to real problems, tuning existing methods to meet your requirements, and playing with samples can you fully understand the possibilities and limitations of any Computer Vision algorithm. This book is specifically designed to get your hands dirty with solving real computer vision tasks. Recipes in this book use OpenCVthe most popular, functionally rich, and widely used open source Computer Vision library. This book progresses from the simplest samples to the most complicated ones, so you will be able to find some useful and information which is easy to understand.