By Steven L. Tanimoto

This booklet explores photo processing from a number of views: the inventive, the theoretical (mainly mathematical), and the programmatical. It explains the elemental rules of photograph processing, drawing on key options and strategies from arithmetic, psychology of belief, machine technology, and paintings, and introduces laptop programming on the way to get extra keep an eye on over photo processing operations. It does so with out requiring college-level arithmetic or previous programming event. The content material is supported through PixelMath, a freely to be had software that is helping the reader comprehend pictures as either visible and mathematical items.

The first a part of the publication covers such subject matters as electronic picture illustration, sampling, brightness and distinction, colour types, geometric ameliorations, synthesizing photographs, stereograms, photomosaics, and fractals. the second one a part of the e-book introduces desktop programming utilizing an open-source model of the easy-to-learn Python language. It covers the fundamentals of photo research and trend reputation, together with side detection, convolution, thresholding, contour illustration, and K-nearest-neighbor category. A bankruptcy on computational images explores such topics as high-dynamic-range imaging, autofocusing, and techniques for instantly inpainting to fill gaps or eliminate undesirable gadgets in a scene. purposes defined comprise the layout and implementation of an image-based video game. The PixelMath software program offers a "transparent" view of electronic pictures by way of permitting the person to view the RGB values of pixels via zooming in on a picture. PixelMath offers 3 interfaces: the pixel calculator; the formulation web page, a sophisticated extension of the calculator; and the Python window.

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Additional resources for An Interdisciplinary Introduction to Image Processing: Pixels, Numbers, and Programs

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Proc. IEEE, Vol. 88, No. 4, pp. 569–587. 7 Exercises 1. In film photography, an image is typically a pattern of pigment or opacity on a paper or film substrate. Adigital image is generally a two-dimensional array of pixels. In either case, a good mathematical model for an image is a function whose domain is a set of coordinate pairs (x, y) and whose range is a set of possible pixel values, such as {v ∈ | 0 ≤ v ≤ 1} representing film opacity values, or the set of RGB triples { (r, g, b) | r, g, b ∈ {0, 1, .

5 Quantization and Dynamic Range We mentioned earlier that there are two key aspects to image representation. The first of these was sampling. The second is quantization. This is the business of assigning to each sample a number (or maybe three separate numbers, in the case of color images) to represent the amount of light at that sample’s location. Quantization always involves some range of values. A typical range is the set of integers from 0 to 255. For a strictly black-and-white image, a range consisting of only 0 and 1 would be good enough.

3 Examining Your Image 35 If you maximize the image window, you’ll be able to see more of the image as you zoom into it. 3 RGB Values Keep zooming in and you should soon be at a scale where the individual pixels will be visible. Each pixel is a small square region of the image. Its color is represented by three numbers: the red component, the green component, and the blue component. We’ll refer to these three components of a pixel as the pixel’s RGB values or its RGB triple. For example, the RGB triple (0, 0, 0) represents the color having 0 units of red, 0 units of green, and 0 units of blue.

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