Image Data Visualization via Local Differential Properties
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Solution Overview
Problem
Current visualization methods for image data sets require significant computational resources due to the need for deriving probabilistic models for geometrical structures, making it costly to visually separate different objects effectively.
Innovation Solution
A visualization apparatus that determines local differential properties for image data sets and assigns visualization properties based on these properties, allowing for the separation of objects without the need for complex probabilistic model calculations, using a system comprising an image data set providing unit, differential property determination unit, assigning unit, and display unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If probabilistic models for geometrical structures are derived and regional responses are computed to visually separate different objects, then object separation quality is improved, but computational costs increase significantly
Solution Approach 1:
The patent extracts only the essential local differential properties (curvature, gradient magnitude, Hessian eigenvalues) needed for object separation, eliminating the need for complex probabilistic models. This extraction approach maintains separation quality while dramatically reducing computational requirements by focusing only on the most relevant geometric features.
Solution Approach 2:
Instead of using complex probabilistic models to infer object properties, the patent inverts the approach by directly computing simple differential properties from the image data and using these to achieve object separation. This inversion from complex inference to direct computation resolves the contradiction between separation quality and computational cost.
2Measurement precision
If complex probabilistic models and regional response computations are performed, then visualization accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the visualization process into independent local differential property computations for each voxel, allowing parallel processing. By computing curvature, gradient magnitude, and Hessian eigenvalues independently for each location, the method achieves accurate visualization without the time-consuming sequential operations of probabilistic models.
Solution Approach 2:
The patent changes the parameters from complex probabilistic distributions to simple differential properties (curvature, gradient, Hessian eigenvalues). This parameter transformation maintains visualization accuracy by capturing essential geometric information while enabling much faster computation through direct mathematical operations.
Data Source
AI summary
The invention relates to a visualization apparatus (1) for visualizing an image data set. The visualization apparatus (1) comprises an image data set providing unit (2) for providing the image data set, a differential property determination unit (5) for determining local differential properties for different regions of the image data set, an assigning unit (6) for assigning visualization properties to the different regions of the image data set depending on the determined local differential properties, wherein a visualization property defines the visualization of a region, to which the visualization property is assigned, and a display unit (7) for displaying the visualization properties assigned to the different regions of the image data set. By displaying the visualization properties assigned to the different regions of the image data set different objects can visually be separated from each other without requiring large computational costs.