Characteristic Point Detection via Distance-Weighted Probability Integration
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Solution Overview
Problem
Existing characteristic point detection methods face challenges in accurately detecting target object features due to variations in posture and biased density of characteristic points, leading to unreliable results, especially when characteristic points are far from reference points.
Innovation Solution
A method that integrates existence probability distributions of characteristic points by weighting them based on their positional relationship, with greater distances receiving smaller weighting factors to minimize the impact of posture variations and biased densities, ensuring accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existence probability distributions are integrated without weighting by distance, then computational complexity is reduced, but detection accuracy deteriorates due to biased density of characteristic points
Solution Approach 1:
The patent applies local quality by introducing distance-dependent weighting factors that vary across different spatial regions. Each existence probability distribution is weighted according to its distance from the reference characteristic point, creating a non-uniform integration scheme that accounts for local variations in point density and spatial relationships.
2Loss of time
If all characteristic points are treated equally in integration, then processing time is reduced, but reliability deteriorates due to posture variations affecting distant points
Solution Approach 1:
The patent changes the parameter of weighting factors from uniform to distance-dependent values. By introducing a parameter that varies with distance from the reference point, the system dynamically adjusts the contribution of each characteristic point based on its spatial relationship, thereby improving reliability without requiring complex per-point analysis.
3Measurement precision
If distance-weighted integration is applied, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-calculating or pre-defining the weighting factors based on distance relationships. This allows the system to establish the integration weights before performing the actual probability distribution integration, reducing the computational burden during the detection phase while maintaining accuracy.
Data Source
AI summary
A characteristic point detection method, including the steps of: detecting a candidate of each of a plurality of characteristic points of a predetermined object from a detection target image; obtaining an existence probability distribution for a target characteristic point with respect to each of the detected candidates of the other characteristic points, which is an existence probability distribution of the target characteristic point when the position of the detected candidate of another characteristic point is taken as a reference, using an existence probability distribution statistically obtained for each combination of two different characteristic points of the plurality of characteristic points; integrating the obtained existence probability distributions by weighting according to the positional relationship between the reference characteristic point and target characteristic point; and estimating the true point of the target characteristic point based on the magnitude of the existence probabilities in the integrated existence probability distribution thereof.