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 snapshot and Video know-how, PSIVT 2011, held in Gwangju, Korea, in November 2011. the full of seventy one revised papers used to be rigorously reviewed and chosen from 168 submissions. the subjects coated are: image/video coding and transmission; image/video processing and research; imaging and pics and visualization; image/video retrieval and scene realizing; biomedical photo processing and research; biometrics and photo forensics; and desktop 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
This quantization is natural because the disparity space (which is proportional to the inverse of the depth) is evenly divided, similar to most stereo methods. In our method, each pixel of the depth map D(p) takes an integer that represents the depth index. The physical depth value for D(p) can be written as zD(p) . Second, the photometric consistency in Eq. (1) should be given over the coordinate system of the target viewpoint. Consequently, we have to map the pixels from the target viewpoint to the input viewpoints in evaluating the consistencies.
A study-speciﬁc unbiased template was constructed from a random subsample of 10 subjects. Using the deformation ﬁeld of warping the individual brain to the template, we deformed the amygdala and hippocampus binary masks to the template space. The normalized masks were then averaged to produce the subcortical masks. The isosurfaces of the subcortical masks are extracted using the marching cube algorithm . The subcortical masks and the corresponding surfaces are shown in Fig. 1. Using ANTS, we have the deformation vector ﬁeld of warping an individual brain to the template.
Then σ S is the sum of L depth plane standard deviations. L σ S = σ l l =1 (8) 3D Perception Adjustment of Stereoscopic Images Based upon Depth Map 17 To reduce the spatial complexity, we define a target standard deviation σ T as follows; σ T = τ ⋅σ S (9) where τ is a user-defined parameter at [0, 1]. 6. Until σ T is achieved, the depth range of depth planes are reduced. The depth planes are sorted according to its standard deviation. The reduction of depth range starts from a depth plane with the greatest standard deviation with a reduction ratio λ .
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.)