Sharif Digital Repository

We extend the Watson image quality metric to 3D images through the concept of integral imaging. In the Watson's model, perceptual thresholds for changes to the DCT coefficients of a 2D image are given for information hiding. These thresholds are estimated in a way that the resulting distortion in the 2D image remains undetectable by the human eyes. In this paper, the same perceptual thresholds are estimated for a 3D scene in the integral imaging method. These thresholds are obtained based on the Watson's model using the relation between 2D elemental images and resulting 3D image. The proposed model is evaluated through subjective tests in a typical image steganography scheme  

Multi-view imaging and display systems has taken a divide and conquer approach to 3D sensing and visualization. We aim to make more reliable and robust automatic feature extraction and natural 3D feature construction from 2D features detected on a pair of frontal and profile view face images. We propose several heuristic algorithms to minimize possible errors introduced by prevalent imperfect orthogonal condition and non-coherent luminance trying to address the problems incurred with illumination discrepancies on common surface points in accommodation of multi-views. In our approach, we first extract the 2D features that are visible to both cameras in both views. Then, we estimate the... 

Multi view imaging has attracted increasing attention recently and has become one of the potential avenues in future video systems. We aim to make more reliable and robust automatic feature extraction and natural 3D feature construction from 2D features detected on a pair of frontal and profile view face images. We propose several heuristic algorithms to minimize possible errors introduced by prevalent non-perfect orthogonal condition and non-coherent luminance. In our approach, we first extract the 2D features that are visible to both cameras in both views. Then, we estimate the coordinates of the features in the hidden profile view based on the visible features extracted in the two... 

Multi view imaging has attracted increasing attention recently and has become one of the potential avenues in future video systems. We aim to make more reliable and robust automatic feature extraction and natural 3D feature construction from 2D features detected on a pair of frontal and profile view face images. We propose several heuristic algorithms to minimize possible errors introduced by prevalent non-perfect orthogonal condition and non-coherent luminance. In our approach, we first extract the 2D features that are visible to both cameras in both views. Then, we estimate the coordinates of the features in the hidden profile view based on the visible features extracted in the two... 

Fluid flow behavior in a porous medium is a function of the geometry and topology of its pore space. The construction of a three dimensional pore space model of a porous medium is therefore an important first step in characterizing the medium and predicting its flow properties. A stochastic technique for reconstruction of the 3D pore structure of unstructured random porous media from a 2D thin section training image is presented. The proposed technique relies on successive 2D multiple point statistics simulations coupled to a multi-scale conditioning data extraction procedure. The Single Normal Equation Simulation Algorithm (SNESIM), originally developed as a tool for reproduction of...