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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

By : Joe Minichino, Joseph Howse
2.1 (7)
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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

2.1 (7)
By: Joe Minichino, Joseph Howse

Overview of this book

OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance. Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.
Table of Contents (11 chapters)
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6
6. Retrieving Images and Searching Using Image Descriptors
10
Index

Summary


This chapter explored the vast and complex topic of video analysis and tracking objects.

We learned about video background subtraction with a basic motion detection technique that calculates frame differences, and then moved to more complex and efficient tools such as BackgroundSubtractor.

We then explored two very important video analysis algorithms: Meanshift and CAMShift. In the course of this, we talked in detail about color histograms and back projections. We also familiarized ourselves with the Kalman filter, and its usefulness in a computer vision context. Finally, we put all our knowledge together in a sample surveillance application, which tracks moving objects in a video.

Now that our foundation in OpenCV and machine learning is solidifying, we are ready to tackle artificial neural networks and dive deeper into artificial intelligence with OpenCV and Python in the next chapter.

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