By Deepak Ghimire, Joonwhoan Lee (auth.), Yo-Sung Ho (eds.)
The two-volume complaints 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 full of seventy one revised papers was once rigorously reviewed and chosen from 168 submissions. the subjects coated are: image/video coding and transmission; image/video processing and research; imaging and pix and visualization; image/video retrieval and scene knowing; biomedical photo processing and research; biometrics and photograph forensics; and laptop 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
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1 Background Super-resolution (SR) reconstruction [3] combines multiple low-resolution images to restore a latent high-resolution image. One of the input images is selected as the basis image to which other input images are registered and for which the resulting high-resolution image is synthesized. Then, an image formation model is established between the input and latent high-resolution images. Finally, by inverting the image formation model with prior knowledge, the latent highresolution image can be restored.
5. 92 4000 7500 7000 6500 6000 5500 5000 4500 4000 3500 male female gender Fig. 6. -G. Kim et al. 03). Since our results are based on the volume of the whole amygdala and hippocampus, it is still unclear if there are any localized shape differences within the parts of amygdala and hippocampus. 2 Localized Subcortical Shape Analysis The length of displacement vector fields along the template surfaces were computed and smoothed as described in section 2. Then Length is regressed over the total brain volume and other variables: Length = β1 + β2 · Brain + β3 · Age + β4 · Gender + (9) where is zero mean Gaussian noise.
Keywords: free-viewpoint image, semi-global stereo, super-resolution, depth reliability, regularization. 1 Introduction Free-viewpoint image synthesis refers to the process of combining a set of multiview images to generate an image from a new viewpoint where no camera was actually located. This technology has attracted much research interest due to its potential for representing 3-D visual information [1]; using this technology, users can fly through the 3-D space and can also display real objects on autostereoscopic 3-D displays with tens of parallax views [2].