Adaptive Curve Fitting via Spatial Blending Factors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional curve fitting techniques often produce unexpected peaks and loops that do not follow the expected flow of data points, requiring manual correction by users, which is inefficient and frustrating.
Innovation Solution
The implementation of blending techniques that compute a blending factor based on the spatial relationship of data points to choose and combine different curve fitting methods, ensuring the curve fits the data points intuitively and maintains slope and curvature continuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional curve fitting techniques are used, then the curve fitting process is simple, but the curve produces unexpected peaks and loops that do not follow the expected flow of data points
Solution Approach 1:
The patent applies dynamics by making the curve fitting technique adaptive and changeable based on local data characteristics. The system dynamically selects between different curve fitting techniques (e.g., Catmull-Rom splines, quadratic Beziers, circular arcs) depending on the spatial relationship of data points, allowing the fitting method to adjust to local conditions rather than applying a single static technique throughout.
Solution Approach 2:
The patent changes parameters by computing a blending factor based on the spatial relationship of data points (e.g., whether points are collinear, form a sharp angle, or are evenly distributed). This blending factor dynamically adjusts the contribution of different curve fitting techniques, transforming the approach from fixed-parameter to variable-parameter curve fitting that adapts to local geometric characteristics.
2Ease of manufacture
If conventional curve fitting techniques are used, then the implementation is straightforward, but manual correction is required which reduces productivity
Solution Approach 1:
The patent implements self-service by enabling the curve fitting system to automatically select and adjust techniques based on the data characteristics without requiring user intervention. The blending factor computation and technique selection happen automatically, allowing the system to serve itself by adapting to the data rather than requiring manual correction or user guidance.
Solution Approach 2:
The patent applies preliminary action by pre-computing the blending factor and selecting the appropriate curve fitting technique before actually generating the curve. This preliminary analysis of spatial relationships and pre-selection of methods eliminates the need for subsequent manual corrections, improving productivity by doing the right thing the first time.
3Device complexity
If a single curve fitting technique is used, then the method is simple to implement, but it cannot adapt to different spatial relationships of data points
Solution Approach 1:
The patent applies segmentation by dividing the curve fitting process into segments that correspond to different spatial relationships of data points. Each segment or local region uses the most appropriate curve fitting technique based on its characteristics (e.g., using circular arcs for evenly spaced points, quadratic Beziers for collinear points), rather than applying a single uniform technique to the entire dataset.
Solution Approach 2:
The patent implements universality by creating a multi-functional curve fitting system that can perform multiple different fitting techniques within a single unified framework. The blending mechanism allows the same system to handle various spatial relationships (collinear points, sharp angles, evenly distributed points) using appropriate techniques, making the system universally applicable to different data configurations.
Data Source
AI summary
Blending techniques for curve fitting are described. In one or more implementations, an indication is received of three or more data points. A blending factor is computed based on a spatial relationship of the three or more data points to each other. A curve is fit to the three or more data points by blending a plurality or curve fitting techniques using the computed blending factor.