By Yongjie Jessica Zhang, João Manuel R. S. Tavares (eds.)
This booklet constitutes the refereed lawsuits of the 4th overseas convention on Computational Modeling of items provided in photographs, CompIMAGE 2014, held in Pittsburgh, PA, united states, in September 2014.
The 29 revised complete papers offered including 10 brief papers and six keynote talks have been conscientiously reviewed and chosen from fifty four submissions. The papers hide the subsequent subject matters: clinical remedy, imaging and research; photo registration, denoising and have id; snapshot segmentation; form research, meshing and graphs; clinical photograph processing and simulations; photograph attractiveness, reconstruction and predictive modeling; image-based modeling and simulations; and computing device imaginative and prescient and data-driven investigations.
Read or Download Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications: 4th International Conference, CompIMAGE 2014, Pittsburgh, PA, USA, September 3-5, 2014 PDF
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Extra resources for Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications: 4th International Conference, CompIMAGE 2014, Pittsburgh, PA, USA, September 3-5, 2014
Automatic objects removal for scene completion. In: IEEE INFOCOM Workshop on Security and Privacy in Big Data (2014) 11. : A novel hybrid image inpainting model. In: International Conference on Audio, Language and Image Processing, pp. 138–142 (2008) 12. : Strategy of computed tomography sinogram inpainting based on sinusoid-like curve decomposition and eigenvector-guided interpolation. Journal of the Optical Society of America A 29(1), 153–163 (2012) 13. : A taxonomy and evaluation of dense two-frame stereo correspondence algorithms.
Consider the k-space image support as a M × M matrix. In a ﬁrst phase, the acquisition process collects a set of random Cartesian trajectories, having a Gaussian distribution in a central region of the k-space whose width is a portion p Adaptive Sampling and Non Linear Reconstruction for Cardiac MRI (a) 27 (b) Fig. 3. CS reconstruction with 115000 samples (a) and diﬀerence between the theoretical image (Figure 1(a)) and CS reconstruction (b) of the k-space, both along the rows and along the columns.
Moreover, it is discussed how the adaptive method is eﬀective in collecting the near optimal number of data to reconstruct the unknown image (at least two times lower than the number ﬁxed by CS) and how the termination strategy is capable to stop the acquisition just to the correct number, adapted to the image shape. Numerical simulations are reported and compared with weighted CS to show its performances. 26 G. Placidi et al. (a) (b) Fig. 2. MSE values calculated on the CS images reconstructed by using increasing datasets from 30000 to 200000 samples (by steps of 5000).