Feature Point Correction via Subspace Projection
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Solution Overview
Problem
Existing methods for determining the position of feature points in image recognition, such as face recognition, face detection, and image interpolation, face challenges in accurately correcting feature points under large fluctuations in face orientation and expression, requiring significant computational resources and lacking effective mechanisms to handle such variations.
Innovation Solution
A data correction apparatus and method that projects vector data onto a subspace for dimension reduction and restoration, determining object fluctuations, and integrating corrected data to improve feature point positioning accuracy, even under large variations, using a combination of projection matrices and Support Vector Machines (SVMs) for fluctuation determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If subspace projection processing is used to correct feature point positions, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the correction process into distinct modules: feature point candidate decision processing, subspace projection processing, dimension reduction processing, subspace inverse projection processing, and distance calculation processing. Each module performs a specific function, making the complex overall process more manageable and implementable while maintaining high correction accuracy for feature point positions
Solution Approach 2:
The patent transforms the high-dimensional feature point coordinate data into a lower-dimensional subspace using dimension reduction processing based on principal component analysis. This dimensionality reduction simplifies the data structure and computational requirements while preserving the essential variance in the data, thereby reducing device complexity without sacrificing manufacturing precision
2Manufacturing precision
If dimension reduction processing is applied to correct feature points, then manufacturing precision is improved, but loss of information increases
Solution Approach 1:
The patent performs preliminary action by storing the projection matrix obtained through principal component analysis in advance. This pre-computed matrix is then reused during the correction process to transform feature point coordinates from the original space to the reduced subspace and back, ensuring that the transformation preserves maximum information while achieving dimension reduction
Solution Approach 2:
The patent implements feedback through the inverse projection process, where the reduced-dimensional data is transformed back to the original coordinate system. The corrected feature point positions are then evaluated by calculating distances between original and corrected positions, providing feedback on the effectiveness of the dimension reduction while minimizing information loss
Data Source
AI summary
A data correction apparatus which corrects data associated with an image of an object projects vector data obtained by connecting data to be corrected to each other onto a subspace to generate a dimensionally reduced projection vector, and executes dimension restoration processing in which the dimensionality of the projection vector is restored to generate dimensionally restored vector data, thereby generating a plurality of dimensionally restored vector data for each type of fluctuation. The data correction apparatus determines the fluctuation of the object based on the projection vector, integrates the plurality of dimensionally restored vector data with each other based on the determination result, and outputs the integration result as corrected data.