Image Processing Lab.
Recent developments in image and video processing along with availability of low cost cameras had led to “3D Computer Vision” techniques. It is a challenging task for newly developing applications and requires dealing with algebraic and geometrical concepts as well as the image and video processing knowledge. It employs the epipolar geometry and the related optimization techniques to compute the camera internal and external parameters in order to achieve the fundamental,essential,and homography transforms among different frames that have been simultaneously captured by multiple cameras. The main applications of 3D computer vision include: 3D scene analysis, 4D pose estimation,4D object tracking,3D model building,4D human action recognition,virtual reality,semantic scene understanding,4D SLAM,multicamera video superresolution,and so forth. In this talk,I will give a brief introduction on 3D computer vision and its various applications.
Dr. Hossein Rabbani
Associate Professor of Isfahan University of Medical Sciences
Medical Image and Signal Processing Research Center
IMAGE MODELING: APPLICATION OF STATISTICAL MODELING IN IMAGE ENHANCEMENT
Modeling is the main core of any signal and system processing and analysis. For example,an image can be seen as a matrix (mathematical/deterministic model) and in this base for a conventional processing such as denoising average operator is used. However,as we know this operator doesn’t lead to an optimum result (the produced image is usually blurred). In another point of view,the image can be seen as a random field (statistical model),and so the denoising process is converted to an estimation problem and better denoising results is achieved. Similarly,we can solve the denoising problem by modeling an image using partial differential equation (PDE),atomic representation (e.g.,x-let transforms),geometric and graph-based methods,etc. The resulted denoising process is completely depended on the proposed model for noise-free image. In this base,we can introduce various methods for modeling of natural images,including mathematical/statistical models,energy-based models,transform-based models,and geometric and graph-based models. In this base,new modeling frameworks can also be achieved using a rational combination of these models. In this lecture after a review on different image modeling methods,the effect of choosing a correct statistical model for Optical Coherence Tomography (OCT) B-scans is investigated based on introducing Gaussianization transform and applying SC-GMM for OCT despeckling.