Defining sharp features in a given 3D model facilitates a better understanding of the surface and aids visualizations, reverse engineering, filtering, simplification, non-photo realism, reconstruction and other geometric processing applications. We present a robust method that identifies sharp features in a point cloud by returning a set of smooth curves aligned along the edges. Our feature extraction is a multi-step refinement method that leverages the concept of Robust Moving Least Squares to locally fit surfaces to potential features. Using Newton's method, we project points to the intersections of multiple surfaces then grow polylines through the projected cloud. After resolving gaps, connecting corners, and relaxing the results, the algorithm returns a set of complete and smooth curves that define the features. We demonstrate the benefits of our method with two applications: surface meshing and point-based geometry compression.

In this chapter, we present a high performance multi-scale 3D image processing framework to exploit the parallel processing power of multiple graphic processing units (Multi-GPUs) for medical image analysis. We developed GPU algorithms and data structures that can be applied to a wide range of 3D image processing applications and efficiently exploit the computational power and massive bandwidth offered by modern GPUs. Our framework helps scientists solve computationally intensive problems which previously required super computing power. To demonstrate the effectiveness of our framework and to compare to existing techniques, we focus our discussions on atlas construction -the application of understanding the development of the brain and the progression of brain diseases.

Defi ning sharp fea tures algorithm processes these points to construct arc-length parame teri zed splin e curves fi t using an iterative refin eme nt method, ali gnin g smooth and continuous curves through the feature points. We demonstrate the benefits of our method with three applications: surface segme ntation, surface meshing and point-based compression. in a 3D model fac ilitates a better understanding of the surface and aids geometric processing and graphics applications, such as reconstruction , filtcring, simplificati on, revcrsc engineering, visualization, and non-photo realism. We present J. Danie ls II (~). T. Ochotta• L. K. Ha • c.T. Silva Scientifi c Computing and Tmaging Tnstitute Universi ty of Utah a robust method that identifies sharp features in a point-based model by returning a set of smooth spline curves aligned along the edges.

Abstract. The purpose of this study is to characterize the neuroanatomical variations observed in neurological disorders such as dementia. We do a global statistical analysis of brain anatomy and identify the relevant shape deformation patterns that explain corresponding variations in clinical neuropsychological measures. The motivation is to model the inherent relation between anatomical shape and clinical measures and evaluate its statistical significance. We use Partial Least Squares for the multivariate statistical analysis of the deformation momenta under the Large Deformation Diffeomorphic framework. The statistical methodology extracts pertinent directions in the momenta space and the clinical response space in terms of latent variables. We report the results of this analysis on 313 subjects from the Mild Cognitive Impairment group in the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Efficient sorting is a key requirement for many computer science algorithms. Acceleration of existing techniques as well as developing new sorting approaches is crucial for many real-time graphics scenarios, database systems, and numerical simulations to name just a few. It is one of the most fundamental operations to organize and filter the ever growing massive amounts of data gathered on a daily basis. While optimal sorting models for serial execution on a single processor exist, efficient parallel sorting remains a challenge. In this paper, we present a hardware-optimized parallel implementation of the radix sort algorithm that results in a significant speed up over existing sorting implementations. We outperform all known General Processing Unit (GPU) based sorting systems by about a factor of two and eliminate restrictions on the sorting key space. This makes our algorithm not only the fastest, but also the first general GPU sorting solution.