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

By Kenneth Dawson-Howe

Explains the idea in the back of uncomplicated computing device imaginative and prescient and gives a bridge from the idea to useful implementation utilizing the general OpenCV libraries
Computer imaginative and prescient is a quickly increasing quarter and it really is changing into gradually more straightforward for builders to use this box because of the prepared availability of top of the range libraries (such as OpenCV 2). this article is meant to facilitate the sensible use of machine imaginative and prescient with the target being to bridge the space among the speculation and the sensible implementation of laptop imaginative and prescient. The ebook will clarify the best way to use the correct OpenCV library workouts and should be followed by means of an entire operating application together with the code snippets from the textual content. This textbook is a seriously illustrated, sensible creation to a thrilling box, the purposes of that are turning into virtually ubiquitous. we're now surrounded via cameras, for instance cameras on desktops & drugs/ cameras outfitted into our cell phones/ cameras in video games consoles cameras imaging tricky modalities (such as ultrasound, X-ray, MRI) in hospitals, and surveillance cameras. This e-book is anxious with aiding the following iteration of desktop builders to use these kind of pictures to be able to enhance structures that are extra intuitive and have interaction with us in additional clever methods.

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A Practical Introduction to Computer Vision with OpenCV, First Edition. Kenneth Dawson-Howe. © 2014 John Wiley & Sons, Ltd. Published 2014 by John Wiley & Sons, Ltd. 1, there can be many local maxima and minima. To reduce this number, the histogram may be smoothed. 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 ?

19) This formula is significantly less expensive (computationally) than the original. Note that the rotating mask can be applied to images with salt and pepper noise, but can result in undesirable effects particularly if noise is present near object boundaries. g. 3x3) centred around the pixel. 20 Non-square mask for use in median filtering in an ordered list. So, for example, if a 3x3 region contained the grey levels (25 21 23 25 18 255 30 13 22), the ordered list would be (13 18 22 21 23 25 25 30 255) and the median would be 23.

2 Noise Models Noise must be joined with the image data in some way. The way in which we model this depends upon whether the noise is data independent or data dependent. e. 8) where g(i, j) is the ideal image, v(i, j) is the noise and f(i, j) is the actual image. 16. e. v(i, j) where g(i, j) is the ideal image, v(i, j) is the noise and f(i, j) is the actual image. 3 Noise Generation In order to evaluate noise, we often need to simulate noise so that it can then be removed/reduced and the extent to which we are successful assessed.

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