Date of Award


Document Type


Degree Name

Master of Science (MS)

Legacy Department

Computer Science

Committee Member

Dr. Donald House, Chair

Committee Member

Dr. Joshua Levine, Co-Chair

Committee Member

Dr. Brian Dean


In volumetric image analysis and visualization, challenges have be induced by the increasing size of volume over recent years. Rendering and interacting with a volume with reduced size is preferable and highly needed. The primary concern in producing such downsized volumetric images is to preserve the important structures and those of the user's interest, such as boundaries between materials. Typical volume reduction approaches usually perform uniform subsampling without the awareness of user-specified parameters such as the opacity and color transfer functions. However, it is also handy for the algorithm to have "global'' encoding and control over the entire volume, meanwhile revealing some features of the data while it is being downsized. This thesis aims at providing a means of such type, extended from the famous seam carving operator that has been used widely in the task of image and video retargeting. Our work applies and extends the seam carving algorithm for videos proposed by Rubinstein et al. to downsize three-dimensional volumetric images. This extended technique computes and removes from the volume two-dimensional seams, or what we name and define as sheets, to reduce the size of the volume with minimum loss of important details measured by gradient. We aim at learning through experimentation the visual quality of seam carved volumetric images, making improvements based on feedback and potentially paving ways towards applications. With the great flexibility of the graph cut formulation, we implement in our algorithm the existing backward and forward energy optimization, and add extensions including isosurface protection and the encoding of the opacity transfer function. At the visual level, experimental results tell us when applied alone with fixed parameters, volumetric seam carving outperforms trivial approaches in preserving important structures only for part of the datasets, on which discussions are included at the best knowledge of the author.



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