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  • Book Overview & Buying Learning OpenCV 3 Computer Vision with Python (Update)
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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

The Kalman filter


The Kalman filter is an algorithm mainly (but not only) developed by Rudolf Kalman in the late 1950s, and has found practical application in many fields, particularly navigation systems for all sorts of vehicles from nuclear submarines to aircrafts.

The Kalman filter operates recursively on streams of noisy input data (which in computer vision is normally a video feed) to produce a statistically optimal estimate of the underlying system state (the position inside the video).

Let's take a quick example to conceptualize the Kalman filter and translate the preceding (purposely broad and generic) definition into plainer English. Think of a small red ball on a table, and imagine you have a camera pointing at the scene. You identify the ball as the subject to be tracked, and flick it with your fingers. The ball will start rolling on the table, following the laws of motion we're familiar with.

If the ball is rolling at a speed of 1 meter per second (1 m/s) in a particular direction...

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Programming languages
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