By Jonas Gomes
Snapshot processing is anxious with the research and manipulation of pictures via desktop. the point of interest of this e-book is to supply an intensive therapy of snapshot processing with an emphasis on these points so much utilized in special effects. all through, the authors pay attention to describing and reading the underlying innovations instead of on offering algorithms or pseudocode. As befits a contemporary advent to this subject, an outstanding stability is struck among discussing the underlying arithmetic of the topic and the most themes coated: sign processing, information discretization, the speculation of color and diversified color structures, operations in pictures, dithering and half-toning, warping and morphing, and photograph processing.
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Additional info for Image Processing for Computer Graphics
2nDn(Xn- kn ~Xn)J. 27. Graph of the sine function in two dimensions. O 1 ••. x2 ... xn. The proof of this n-dimensional version of the Shannon-Whittaker sampling theorem is analogous to that of the one-dimensional version. The comments we made earlier about sampling, reconstruction, and aliasing are also valid here. When we study image signals in Chapter 6, we will return to these problems and give several examples. 4 to the discrete universe. Here a signal f is given by a representation sequence (...
The Shannon b asis can be used as an efficient discretization m ethod for the solution of many numerical problems. The reader interested in this topic can consult (Lund and Bowers 1992), which contains a proof that the Shannon basis is a complete orthonormal set in an appropriately chosen signal space. References Bracewell, R. (1986). The Fourier Transform and its Applications, second ed. McGraw-Hill, New York. Briggs, W. L. and H enson, V. E. ( 1995). The DFT: An Oumer's Manual for the Discrete Fourier Transform.
The number of samples per unit of length in each direction of space is called the sampling rate or sampling frequency in that direction. 12), there is usually loss of information under point sampling. Here we are interested in two problems: • Under what conditions can we avoid loss of information when performing point sampling? 1 3)? • Assuming that the sampling was lossless, how can the original signal be reconstructed from the samples? What interpolation method should be used? These two questions can be subsumed under one: under what conditions does point sampling give rise to an exact linear representation?