Read e-book online Advances in Image and Video Technology: 5th Pacific Rim PDF

By Deepak Ghimire, Joonwhoan Lee (auth.), Yo-Sung Ho (eds.)

ISBN-10: 3642253660

ISBN-13: 9783642253669

The two-volume court cases LNCS 7087 + LNCS 7088 represent the court cases of the fifth Pacific Rim Symposium on picture and Video know-how, PSIVT 2011, held in Gwangju, Korea, in November 2011. the entire of seventy one revised papers was once rigorously reviewed and chosen from 168 submissions. the themes lined are: image/video coding and transmission; image/video processing and research; imaging and photographs and visualization; image/video retrieval and scene realizing; biomedical photo processing and research; biometrics and snapshot forensics; and desktop imaginative and prescient applications.

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Read Online or Download Advances in Image and Video Technology: 5th Pacific Rim Symposium, PSIVT 2011, Gwangju, South Korea, November 20-23, 2011, Proceedings, Part I PDF

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Extra info for Advances in Image and Video Technology: 5th Pacific Rim Symposium, PSIVT 2011, Gwangju, South Korea, November 20-23, 2011, Proceedings, Part I

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Although solving the eigenfunctions of the Laplace-Beltrami operator requires the finite element method, the proposed method is analytic in a sense that heat kernel smoothing is formulated as a series expansion explicitly. We are not claiming our framework to be analytic which is theoretically impossible when dealing with real data. The proposed method represents isotropic heat diffusion analytically as a series expansion so it avoids the numerical instability associated with solving the diffusion equations numerically [20,22,27].

Let Ylm be the spherical harmonic of degree l and order m [34]. Due to the multiplicity, there are 2l+1 eigenfunctions Yl,−l , · · · , Yl,l corresponding to the same eigenvalue l(l + 1). Further, any linear combination lm=−l βlm Ylm is an eigenfunction as well. So it is not possible to validate the accuracy of the obtained eigenfunctions. Therefore, we only checked if solving (5) produces the expected eigenvalues. -G. Kim et al. 5 0 0 50 100 150 200 250 Fig. 4. Left: 133 eigenvalues are numerically computed (blue dotted) and compared against the ground truth (red solid) λl = l(l + 1) for up to degree l = 11.

The reduction of depth range starts from a depth plane with the greatest standard deviation with a reduction ratio λ . The following equation explains how the range of a depth plane is reduced. For a l l range [ Dl , Dmax ], its depth range is adjusted into [ E l , Emax ] as follows: min min l l l l Emin = (1 + λ ) Dmin Emax = (1 − λ ) Dmax (10) The depth plane adjustment algorithm is implemented by the following iterative method: Given L input depth planes, Step 1: σ l is computed for each depth plane.

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Advances in Image and Video Technology: 5th Pacific Rim Symposium, PSIVT 2011, Gwangju, South Korea, November 20-23, 2011, Proceedings, Part I by Deepak Ghimire, Joonwhoan Lee (auth.), Yo-Sung Ho (eds.)

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