By Kenneth Dawson-Howe

ISBN-10: 1118848454

ISBN-13: 9781118848456

Explains the idea at the back of simple desktop imaginative and prescient and gives a bridge from the idea to functional implementation utilizing the usual OpenCV libraries

Computer imaginative and prescient is a speedily increasing region and it really is changing into gradually more uncomplicated for builders to use this box as a result of the prepared availability of top of the range libraries (such as OpenCV 2). this article is meant to facilitate the sensible use of laptop imaginative and prescient with the aim being to bridge the space among the speculation and the sensible implementation of machine imaginative and prescient. The ebook will clarify the right way to use the appropriate OpenCV library exercises and may be followed by way of an entire operating software together with the code snippets from the textual content. This textbook is a seriously illustrated, sensible advent to an exhilarating box, the functions of that are changing into nearly ubiquitous. we're now surrounded through cameras, for instance cameras on desktops & capsules/ cameras equipped into our cell phones/ cameras in video games consoles cameras imaging tough modalities (such as ultrasound, X-ray, MRI) in hospitals, and surveillance cameras. This booklet is worried with aiding the following new release of laptop builders to use these kinds of pictures which will advance structures that are extra intuitive and have interaction with us in additional clever methods.

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**Additional info for A Practical Introduction to Computer Vision with OpenCV**

**Example text**

These approaches select rather crude subspaces within the colour spaces in which they are defined. A better approach to this problem is to: 1. Obtain a representative sample set of the colour(s) to be selected. 2. Create a histogram of the samples. 3. 0. e. the probability that a pixel with the corresponding colour is from the sample set). 4. e. p(i, j) = h(f (i, j))). 8 Back-projection (right image) of a (3D) HLS histogram of the skin pixels (centre image) of a colour image (left image). e. 8, in which a sample set of skin pixels is histogrammed and back-projected into an image in order to identify skin regions in the image.

9). 3. 9 is the original k-means clustering algorithm. g. see (Kanungo, Mount, Netanyahu, Piatko, Silverman, and Wu, 2002)). e. specific colours) to best represent all of the colours in an image, and that number (k) is specified in advance. The colour of each pixel in the image is referred to as a pattern. A group of patterns associated with a particular exemplar is referred as a cluster. r The starting exemplars are, for example, r r ◦ picked randomly ◦ chosen as the first k patterns ◦ distributed evenly.

E. less iterations will be necessary). Rotating mask smoothing is not supported in OpenCV, but is quite easy to implement. Averaging with a rotating mask is quite effective at suppressing noise and sharpening the image (edges), although it will be significantly slower than simple local averaging. 18 (d) for examples. 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.

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

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