By Deepak Ghimire, Joonwhoan Lee (auth.), Yo-Sung Ho (eds.)
The two-volume court cases LNCS 7087 + LNCS 7088 represent the lawsuits of the fifth Pacific Rim Symposium on picture and Video expertise, PSIVT 2011, held in Gwangju, Korea, in November 2011. the full of seventy one revised papers was once conscientiously reviewed and chosen from 168 submissions. the themes coated are: image/video coding and transmission; image/video processing and research; imaging and snap shots and visualization; image/video retrieval and scene realizing; biomedical photograph processing and research; biometrics and snapshot forensics; and computing device imaginative and prescient applications.
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Extra resources for Advances in Image and Video Technology: 5th Pacific Rim Symposium, PSIVT 2011, Gwangju, South Korea, November 20-23, 2011, Proceedings, Part I
4 shows the 133 computed eigenvalues compared against the ground truth. 32%). We also checked the accuracy of the constructed heat kernel. On a unit sphere, the heat kernel is given by ∞ l Kσ (p, q) = e−l(l+1)σ Ylm (p)Ylm (q). (7) l=0 m=−l We have taken the degree l = 85 expansion as the ground truth and compared it to the numerically constructed heat kernel. 5 (Fig. 4). The rate of convergence depends on the bandwidth. As the number of eigenfunctions increases, the constructed heat kernel converge to the ground truth quickly.
Once we obtained the basis functions ψj numerically, we need to estimate the Fourier coeﬃcients βj . It can be shown that the Fourier coeﬃcients can be estimated as βj = Y Aψ j , (6) where Y = (Y (p1 ), · · · , Y (pn )) and ψ j = (ψj (p1 ), · · · , ψj (pn )) . wisc. edu/~chung/lb/. 4 Validation The heat kernel smoothing framework is validated on a unit sphere where the Laplace-Beltrami eigenfunctions are exactly given as spherical harmonics. We used a spherical mesh with 40,962 uniformly sampled mesh vertices.
Finally, a stereoscopic image is generated by 2D+Depth method. Fig. 1. 1 Spatial Complexity Spatial complexity of a depth map is measured by calculating a standard deviation of pixel depth values. The reason behind using the standard deviation for the measurement of spatial complexity is that it is the measure of the dispersion or variability of a set of values around the mean of that set . Thus, if the depth map has high spatial complexity, the standard deviation of the pixel depth values is expected to be high.