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On the right, a region of the middle image (10x10 pixels) have been copied and resized to a larger picture. In the middle, result of our program generating a random pattern from three colors picked up in the source image. įigure 1: on the left our sample pattern (photograph of a rock). However for now all we can do to create variation from pixel to pixel is to use the C drand48() function which returns a random value in the range. The first obvious solution to this problem is to open this image with Photoshop for instance, and to pick up each one of these three average colors in order to be able to reuse them in a program to procedurally generate a pattern that looks more or less similar to our reference picture. These colors are more or less distributed in equal amount across the surface of the rock. This example is interesting because we can observe that the rock pattern is made of three main colors: green, pink and grey. All we have is a plane which without texturing, looks completely flat (uniform color). You can't use textures (the most obvious solution is to take this image and map it on a plane). Here is an example: let's observe the image of a real rock (figure 1 left) and let's assume that our task is to create a CG image that reproduces the look of this object. This would be unsuitable for introducing a slight variation to the visual appearance of two points that are spatially close. Therefore calling this function is likely to produce two very different numbers. Every time we call them they return numbers which are not related (uncorrelated) to each other. In other words local changes are gradual, while global changes can be large. But two points on the surface of a same object which are far apart can look very different. Two points on the surface of a real object usually look almost the same when they are fairly close to each other. Random patterns we can observe in nature are usually smooth. However using a RNG (random number generator) to add variation to the appearance of a 3D object isn't sufficient. In programming we usually use random number generators whenever we need to create random numbers. They needed something to break this clean look by modulating visual properties (color, shininess) of objects across their surface. Objects that were rendered with solid colors looked too clean. People working in CG studios started to look for alternative solutions. However in the mid 80's, computers had a very limited memory and images used for texturing would not easily fit in RAM. We can map objects with images to add visual complexity to their appearance. Noise was developed in the mid 80's as an alternative to using images for texturing objects. The website offers many lessons where each topic mentioned in this lesson can be studied individually (aliasing, texture generation, complex noise functions, landscape cloud and water surface generation, as well as a few advanced programming techniques such bitwise arithmetic, hashing, etc.). This is just a brief introduction to noise and a few of its possible applications. Keep in mind while reading this lesson that we will be overlooking many techniques which are too complex to be fully studied here. To create some images and experiment with various parameters, we will implement a simple (but fully functional) version of the noise known as value noise. Using it right requires an understanding of how it works and how it is made. Noise is not a complicated concept to understand, but it has many subtleties. You will learn what noise is, what its properties are and what you can do with it. This lesson explains the concept of noise in a very simple (almost naive) form.
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"To the physicist, unpredictable changes of any quantity V varying in time t are known as noise." (The Science of Fractal Images, Richard F.