Bio-Inspired Sampling and Reconstruction Framework for Scientific Visualization

Abstract

This project investigated sampling theoretic issues that arise in visualization of 3-D (e.g., in simulation or biomedical) datasets. As sampling and reconstruction are key elements in the visualization pipeline, their mathematical modeling and analysis are foundational to reliability of the resulting visualizations. An important achievement of this investigation is establishing the superiority of optimal lattices for reconstruction of scattered data. These lattices are observed in nature as crystallographic structures (e.g., body centered cubic and face centered cubic lattices), as well as biological vision systems (e.g., hexagonal lattice). While the sampling theoretic advantages of optimal lattices have been established, practical tools (e.g., filtering, interpolation, wavelet analysis, signal reconstruction, regularization methods) for exploiting these advantages have been unavailable to practitioners.

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Document Details

Document Type
Technical Report
Publication Date
Sep 17, 2015
Accession Number
ADA621621

Entities

People

  • Alireza Entezari

Organizations

  • University of Florida

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Compressed Sensing
  • Crystal Lattices
  • Crystal Structure
  • Cubic Lattices
  • Data Analysis
  • Data Sets
  • Data Visualization
  • Department Of Defense
  • Geometry
  • Information Processing
  • Interpolation
  • Probabilistic Models
  • Probability
  • Three Dimensional
  • Topology
  • Visualizations

Fields of Study

  • Engineering

Readers

  • Computational Fluid Dynamics (CFD)
  • Computer Vision.
  • Systems Analysis and Design

Technology Areas

  • Biotechnology