David Millán Escrivá - Building Computer Vision Projects with OpenCV 4 and C++

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First published: March 2019

Production reference: 1220319

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David Milln Escriv was eight years old when he wrote his first program on an 8086 PC using the BASIC language. He completed his studies in IT from the Universitat Politcnica de Valencia with honors in human-computer interaction supported by computer vision with OpenCV (v0.96). He has a master's degree in artificial intelligence, computer graphics, and pattern recognition, focusing on pattern recognition and computer vision. He also has more than nine years' experience in computer vision, computer graphics, and pattern recognition. He is the author of the Damiles Blog, where he publishes articles and tutorials on OpenCV, computer vision in general, and optical character recognition algorithms.

Prateek Joshi is an artificial intelligence researcher, an author of eight published books, and a TEDx speaker. He has been featured in Forbes 30 Under 30, CNBC, TechCrunch, Silicon Valley Business Journal, and many more publications. He is the founder of Pluto AI, a venture-funded Silicon Valley start-up building an intelligence platform for water facilities. He graduated from the University of Southern California with a Master's degree specializing in Artificial Intelligence. He has previously worked at NVIDIA and Microsoft Research.

Vincius G. Mendona is a computer graphics university professor at Pontifical Catholic University of Paran (PUCPR). He started programming with C++ back in 1998, and ventured into the field of computer gaming and computer graphics back in 2006. He is currently a mentor at the Apple Developer Academy in Brazil, working with, and teaching, metal, machine learning and computer vision for mobile devices. He has served as a reviewer on other Pack books, including OpenNI Cookbook, and Mastering OpenCVand Computer Vision with OpenCV 3 and Qt5. In his research, he has used Kinect, OpenNI, and OpenCV to recognize Brazilian sign language gestures. His areas of interest include mobile, OpenGL, image processing, computer vision, and project management.

Roy Shilkrot is an assistant professor of computer science at Stony Brook University, where he leads the Human Interaction group. Dr. Shilkrot's research is in computer vision, human-computer interfaces, and the cross-over between these two domains, funded by US federal, New York State, and industry grants. Dr. Shilkrot graduated from the Massachusetts Institute of Technology (MIT) with a PhD, and has authored more than 25 peer-reviewed papers published at premier computer science conferences, such as CHI and SIGGRAPH, as well as in leading academic journals such as ACM Transaction on Graphics (TOG) and ACM Transactions on Computer-Human Interaction (ToCHI).

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OpenCV is one of the best open source computer vision libraries available to developers. With OpenCV, developers can create complete projects for image processing, object detection, and motion detection. This learning path is for absolute beginners who wish to learn how to build OpenCV projects from scratch with working code samples. We will begin with the introduction on computer vision and its basic concepts such as filtering, histograms, Object segmentation, and object detection. As you progress through the course you will then dig deeper into image processing exploring various computer vision algorithms and understand how the latest advancement in machine learning and deep learning enhances the process of object detection. You will put this knowledge to practice by building real-world computer vision applications as you progress through the course.
Later you will get acquainted with the API functionality of OpenCV and gain insights into design choices in a complete computer vision project. You'll also go beyond the basics of computer vision to implement solutions for complex image processing projects such as skin color analysis, face landmark and pose estimation, Augmented reality applications, and Number plate recognition. Finally, towards the end of the learning path, you will learn about certain best practices and common pitfalls to avoid while building computer vision applications.