Download Digital Image Warping by George Wolberg PDF
By George Wolberg
This best-selling, unique textual content makes a speciality of photograph reconstruction, real-time texture mapping, separable algorithms, two-pass transforms, mesh warping, and lighting tricks. The textual content, containing all unique fabric, starts with the heritage of the sector and maintains with a evaluation of universal terminology, mathematical preliminaries, and electronic snapshot acquisition. Later chapters speak about equations for spatial details, interpolation kernels, filtering difficulties, and fast-warping thoughts in keeping with scanline algorithms.
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Additional resources for Digital Image Warping
This is usually integrated as part of a mechanical scanner (more on this later) whereby some mechanical assembly moves the line sensor across the entire physical image. Area sensors are composed of a 2-D matrix of photosites. CCDs have several advantages over vidicon systems. The chief beneﬁts are derived from the extremely linear radiometric (intensity) response and increased sensitivity. Unlike vidicon systems that can yield no more'than 8 bits of precision because of analog noise, a CCD can easily provide 12 bits of precision.
It decomposes f (x) into a sum of complex exponentials. The complex function F (u) speciﬁes, for each frequency u. the amplitude and phase of each complex exponential. F(u) is commonly known as the signal’s frequency spectrum. This should not be confused with the Fourier transform of a ﬁlter, which is called the frequency response (for 1-D ﬁlters) or the modulation transfer function (for 2-D ﬁlters). The frequency response of a ﬁlter is computed as the Fourier transform of its impulse response.
It is important to realize that f (x) and F(u) are two different representations of the same function. In particular. f (x) is the signal in the spatial domain and F (u) is its counterpart in the frequency domain. One goes back and forth between these two representations by means of the Fourier transform pair. The transformation from the fre- quency domain back to the spatial domain is given by the inverse Fourier transform, deﬁned in Eq. 14). , standard color images. The Fourier transform of a real function is usually complex.