Topological and Classifier Cases for Gap-Free Raster Contours
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Solution Overview
Problem
Existing contour map generation techniques for combining raster and classification data suffer from inaccuracies such as gaps and ramps, and are computationally inefficient, especially on devices with limited processing power like mobile computing devices.
Innovation Solution
A method involving the computation of topological and classifier cases for cells in a superimposed grid to generate contour segments, which are then combined into contours, ensuring accurate representation without gaps or ramps, and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing contour map generation techniques combine raster and classification data, then contour maps can display both numerical quantities and classification information, but inaccuracies such as gaps and ramps appear in the generated contours
Solution Approach 1:
The patent segments the contour generation process into distinct phases: generating contour segments for each class separately, then combining them into complete contours. This segmentation allows each class's contour segments to be generated independently using the marching squares algorithm, avoiding the mixing of different class data that causes gaps and ramps, while still achieving the ultimate goal of displaying multiple classes in a single contour map.
Solution Approach 2:
The patent applies preliminary action by first generating contour segments for each individual class before combining them. The marching squares algorithm is applied preliminarily to each class separately to create accurate contour segments, which are then assembled into complete contours. This preliminary class-by-class processing ensures accuracy before the final combination step.
2Adaptability or versatility
If existing techniques generate contour maps with classification data, then comprehensive geographic and weather information can be displayed, but computational complexity increases significantly
Solution Approach 1:
The patent segments the computational workload by processing each class separately through the marching squares algorithm. Instead of handling all classes simultaneously in a complex unified process, the system divides computation into independent class-specific contour segment generation tasks, reducing the overall computational complexity while maintaining the ability to display multiple classes.
Solution Approach 2:
The patent applies partial action by generating contour segments only for the current class being processed, rather than attempting to generate complete contours for all classes at once. This partial processing approach reduces memory requirements and computational overhead at each step, making the system feasible for mobile devices with limited resources.
3Loss of information
If contour maps display multiple classes of data, then more comprehensive information is provided to users, but processing time and computational resources increase
Solution Approach 1:
The patent segments information processing by handling each class independently through separate contour segment generation passes. This segmentation allows the system to process and display comprehensive multi-class information while managing processing time through efficient, repeated application of the marching squares algorithm to each class, rather than using more complex unified processing approaches.
Data Source
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AI summary
A computing device (10) including one or more processing devices (12) configured to receive raster data (20) including raster data values (22). The processing devices obtain classifier grid data (30) including classification values (36) selected from among two or more classes (36). The processing devices generate a contour map (66) by, for each of the classes, generating contour segments (60) connecting pairs of grid locations (24) included in the raster data. Generating the contour segments includes computing respective topological cases (50) of cells (48) of a superimposed grid (46), which includes cells of the raster data that are associated with respective classification values. Generating the contour segments further includes computing respective classifier cases (52) of the cells. The contour segments are computed based on the topological cases and classifier cases of the cells and are combined into contours (64). The contour map is computed as a visual representation of the contours. The processing devices output the contour map for display at a display device (18).