Read e-book online Advances in Computers, Vol. 77 PDF

By Marvin V. Zelkowitz

ISBN-10: 0123748127

ISBN-13: 9780123748126

This can be quantity seventy seven of Advances in desktops. considering 1960, annual volumes are produced containing chapters through the various best specialists within the box of pcs at the present time. for fifty years those volumes supply principles and advancements which are altering our society. This quantity provides 8 diversified subject matters protecting many various facets of machine technology. quite a lot of matters are lined from insights into different methods participants can engage with digital units to how universal legislations is adapting to and impacting on the net.

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The probability of observing a sample f (x, y) knowing it was generated from model M1 is given by 2  2 3 PN f ð x; y Þ À a f ð x þ u; y þ v Þ u; v ¼ ÀN u; v 1 6 7 Prf f ðx; yÞj f ðx; yÞ 2 M1 g ¼ pffiffiffiffiffiffi exp4À 5: 2 2s s 2p ð65Þ 36 H. FARID The variance, s2 , of this Gaussian distribution is estimated in the M-step. A uniform distribution is assumed for the probability of observing a sample generated by the outlier model, M2 , that is, Prf f ðx; yÞj f ðx; yÞ 2 M2 g is equal to the inverse of the range of possible values of f(x, y).

As before, the difference between c1 and c2 will be minimal when q2 ¼ q1 . But, since the coefficients were initially quantized by q0 , where q0 > q1 , we expect to find a second minimum when q2 ¼ q0 . Shown in Fig. 17B is the sum of squared differences between c1 and c2 , as a function of q2 , where q0 ¼ 23 and q1 ¼ 17. As before, this difference increases as a function of increasing q2 , reaches a minimum at q2 ¼ q1 ¼ 17, and most interestingly has a second local minimum at q2 ¼ q0 ¼ 23. We refer to this second minimum as a JPEG ghost, as it reveals that the coefficients were previously quantized (compressed) with a larger quantization (lower quality).

Where M is the matrix containing the sampled spherical harmonics, v is the vector of ! unknown lighting environment coefficients, and b is the vector of intensities at p points. The least-squares solution to this system is ! À ÁÀ1 v ¼ MT M MT b : ! ð43Þ This solution requires 3D surface normals from at least nine points on the surface of an object. Without multiple images or known geometry, however, this requirement may be difficult to satisfy from an arbitrary image. As in [5, 12], we observe that under an assumption of orthographic projection, the z-component of the surface normal is zero along the occluding contour of an object.

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Advances in Computers, Vol. 77 by Marvin V. Zelkowitz

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