Feature Point Selection for 3D Recognition Accuracy
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Solution Overview
Problem
Selecting adequate feature points for recognition tasks on three-dimensional shape models is challenging due to subjective estimation of importance and accuracy, which varies with object type, extraction method, and image conditions, making it difficult to achieve maximum recognition performance.
Innovation Solution
A feature point selecting system that evaluates recognition error and determines feature point importance using a cost function, repeatedly executing recognition tasks and adjusting importance until convergence, to select feature points that minimize recognition error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a great number of feature points are used to improve recognition performance, then recognition accuracy is improved, but computation amount increases
Solution Approach 1:
The patent extracts only the essential feature points from a large set of candidate feature points by evaluating their contribution to recognition accuracy. This extraction process identifies and retains only those feature points that significantly impact recognition performance, discarding redundant points to reduce computation while maintaining accuracy.
Solution Approach 2:
The patent applies different importance weights to different feature points based on their local characteristics and contribution to recognition. Rather than treating all feature points equally, the system evaluates each point's specific value and assigns corresponding weights, allowing optimization of the feature point set for both accuracy and computational efficiency.
2Measurement precision
If feature points are manually determined to satisfy both importance and extraction accuracy conditions, then recognition performance is improved, but system complexity and manual effort increase
Solution Approach 1:
The patent implements an automated evaluation system that self-determines the importance of each feature point based on predefined criteria. The system automatically calculates importance weights by evaluating how each feature point contributes to recognition accuracy, eliminating the need for manual determination while maintaining optimal recognition performance.
Solution Approach 2:
The patent changes the parameter of feature point selection from manual coordinate specification to automated importance weight calculation. By transforming the selection criteria into quantifiable parameters that can be automatically evaluated, the system replaces manual complex determination with computational parameter assessment.
3Ease of operation
If feature points are selected based on subjective estimation of importance and accuracy, then selection process is simplified, but recognition performance varies and cannot achieve maximum
Solution Approach 1:
The patent introduces a feedback mechanism where the importance of each feature point is evaluated based on its actual contribution to recognition accuracy. The system calculates importance weights by measuring the impact of each feature point on recognition performance, providing objective feedback that replaces subjective estimation while maintaining ease of operation through automated evaluation.
Data Source
AI summary
A recognition task executing means 11 that provides a feature point selecting system which can select an adequate feature point matching a recognition algorithm in a recognition task executes the recognition task using an importance of each of a plurality of feature point candidates on a three-dimensional shape model for a plurality of evaluation images. A recognition error evaluating means 12 evaluates a recognition error related to all evaluation images from a difference between a recognition result of the recognition task and correct data of the recognition task for each evaluation image. A feature point importance determining means 13 sets a cost function which is represented as a function obtained by adding a restriction condition that an importance of an unimportant feature point candidate becomes close to zero, to the recognition error related to all evaluation images, and calculating the importance of each feature point candidate which minimizes a value of the cost function. A feature point selecting means 14 selects a feature point which needs to be used in the recognition task from the feature point candidates on the three-dimensional shape model based on the importance of each feature point candidate.