A Practical Introduction to Computer Vision with OpenCV by Kenneth Dawson-Howe

By Kenneth Dawson-Howe

Explains the speculation at the back of easy machine imaginative and prescient and gives a bridge from the speculation to sensible implementation utilizing the typical OpenCV libraries
Computer imaginative and prescient is a speedily increasing sector and it's changing into gradually more straightforward for builders to use this box as a result of the prepared availability of top quality libraries (such as OpenCV 2). this article is meant to facilitate the sensible use of machine imaginative and prescient with the objective being to bridge the distance among the speculation and the sensible implementation of desktop imaginative and prescient. The e-book will clarify the way to use the proper OpenCV library exercises and should be observed by means of an entire operating application together with the code snippets from the textual content. This textbook is a seriously illustrated, useful creation to a thrilling box, the purposes of that are changing into nearly ubiquitous. we're now surrounded via cameras, for instance cameras on desktops & pills/ cameras equipped into our cellphones/ cameras in video games consoles cameras imaging tough modalities (such as ultrasound, X-ray, MRI) in hospitals, and surveillance cameras. This e-book is worried with supporting the subsequent new release of computing device builders to use a lot of these pictures with a purpose to strengthen structures that are extra intuitive and engage with us in additional clever methods.

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This is done by creating a new array of values where each value is the average of a number of values centred on the corresponding value in the original histogram. This process is often referred to as filtering. 2 for an example. Histograms 37 10 20 30 25 20 40 50 40 20 20 ? 20 25 25 28 37 43 37 27 ? at(i+1)) / 3; One major question arises when doing this filtering (the same question arises with all filtering operations in image processing): what do we do at the ends of the histogram? Unfortunately there is no correct answer and typical solutions include: r Altering the filter so that only supported values are included.

For example, most of the red points representing the ball in the snooker image have a high value of saturation as well as a limited range of hue value. g. 5). 5 A 3D histogram (right) of the RGB channels from the colour image on the left. Note that the 0,0,0 point is shown on the front layer at the bottom left. The green axis goes from bottom left to bottom right of the front layer, the blue axis goes from bottom left to top left of the front layer and the red axis goes from bottom left of the front layer to bottom left of the bottommost layer.

In OpenCV, CMY is not directly supported but we could convert to CMY values by using the InvertColour routine shown previously. 9). 9 YUV image (top left) shown with luminance (Y) channel (top right), U channel (bottom left) and V channel (bottom right) the amount of data that needs to be transmitted. For example, in YUV420p format 4 bytes of luminance (Y) are transmitted for every 2 bytes of chrominance (1 U and 1 V). g. ). The luminance typically ranges from 0 to 1. The hue describes the colour and ranges from 0 to 360◦ .

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