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Master Computer Vision™ OpenCV4 in Python and Machine Learning [Video]

More Information
Learn
  • How to build complex Computer Vision applications using the latest techniques in OpenCV
  • Use Machine Learning and Augmented Reality in Computer Vision
  • Face detection and recognition (face swapping and filters!)
  • Object detection, tracking, and motion analysis
  • Handwriting recognition
  • Programming skills such as basic Python and NumPy
  • Understand how to use computer vision when executing cool startup ideas
About

Computer vision applications and technology are exploding right now, with several apps and industries making amazing use of the technology—ranging from up-and-coming apps such as MSQRD, and PRISMA to billion-dollar apps such as Pokémon GO and Snapchat! Even Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily utilizing computer vision for face and object recognition, image searching, and especially in self-driving cars! As a result, the demand for computer vision expertise is growing exponentially! However, learning computer vision is hard! Existing online tutorials, textbooks, and free MOOCs are often outdated, using older and incompatible libraries, or are too theoretical, making the subject difficult to understand.

This was the author's problem when learning Computer Vision and it became incredibly frustrating. Even simply running example code found online proved difficult as libraries and functions were often outdated. The author created this course to teach you all the key concepts without the heavy mathematical theory—all the while using the most up-to-date methods. At the end of the course, you will be able to build 12 awesome Computer Vision apps using OpenCV (the best supported open-source computer vision library that exists today!) in Python. Using it in Python is just fantastic as Python allows us to focus on the problem at hand without getting bogged down in complex code. If you're an academic or college student but want to learn more, the author still points you in the right direction by linking the research papers for techniques used. So if you want to get an excellent foundation in Computer Vision, look no further. This is the course for you!

The code bundle for this video course is available at https://github.com/PacktPublishing/Master-Computer-Vision-OpenCV3-in-Python-and-Machine-Learning

Style and Approach

Learn Computer Vision using OpenCV in Python, using the latest 2018 concepts, and implement 12 awesome projects! In this course, you will discover the power of OpenCV in Python, and obtain the skills to dramatically increase your career prospects as a Computer Vision developer.

Features
  • Perform image manipulations.
  • Segment images by understanding contour, circle, and line detection. You'll even learn how to approximate contours, and perform contour filtering and ordering as well as approximations.
  • Use feature detection (SIFT, SURF, FAST, BRIEF and ORB) to do object detection.
  • Implement object detection for faces, people, and cars.
  • Extract facial landmarks for face analysis, applying filters and face swaps.
  • Implement Machine Learning in Computer Vision for handwritten digit recognition.
Course Length 6 hours 19 minutes
ISBN 9781789616521
Date Of Publication 24 Oct 2018

Authors

Rajeev Ratan

Rajeev Ratan is a Computer Vision Expert, Data Scientist, and Electrical Engineer. He has a BSc in Computer and Electrical Engineering and an MSc in Artificial Intelligence from the University of Edinburgh, where he gained extensive knowledge about machine learning, Computer Vision, and intelligent robotics. He has published research on using data-driven methods for probabilistic stochastic modeling and was even part of a group that won a robotics competition at the University of Edinburgh. He started his own computer vision startup based around deep learning and contributed to two more startups in the Computer Vision domain. Previously, he worked at two of the Caribbean's largest telecommunication operators, where he gained experience in managing technical staff and deploying complex telecommunication projects.