Learning Structured Models for Segmentation of 2-D and 3-D Imagery

3 D Exploitation of 2 D Imagery

The invention of the digital camera is attributed to Eastman Kodak engineer Steven Sasson in 1975 [1]. Sasson’s device recorded 0.01-megapixel black-and-white pictures to cassette tapes. The first digital photograph required 23 seconds to generate and needed a television set to display [2]. Today, digital cameras recording greater than 10-megapixel color photos to Secure Digital (SD) cards with...

Elastic deformable contour and surface models for 2-D and 3-D image segmentation

Learning recognition and segmentation of 3-D objects from 2-D images

A framework called Cresceptron is introduced for $automatic algorithm design through leaming of con-Icepis and rules, thus deviating f r o m the traditional :mode in which humans specify the rules comprising 1% vision algorithm. With the Cresceptron, humans lis designers need only t o provide a good structure for !earning, but they are relieved of most design details. The Cresceptron has been t...

Automatic Segmentation of 3 D Cardiac Spect Imagery

In this paper, we have presented an automated technique to segment transaxial data in 3D cardiac SPECT studies.The proposed methodology incorporates a priori knowledge of the LV structure and selectively employs various image processing and computer vision techniques to extract voxels corresponding to the left ventricle from the 3D dataset. This segmentation approach is an inherent part of a sy...

Generative Models for 2-D images of 3-D scenes

Generative Models for 2-D images of 3-D scenes Anitha Kannan Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2007 This thesis introduces generative models of appearance for analyzing 2D images of 3D visual scenes. Many 2D images contain multiple objects, where image components corresponding to each object undergo deformations, eg., due to changes in the shape ...