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08:04
Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
Published on: December 4, 2013
Identification of space curves from two-dimensional perspective views.
1Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Summary
This study introduces a novel method for matching 3D objects with curved surfaces to 2D views. It uses Fourier transforms of curvature functions derived from spline representations for accurate shape matching.
Area of Science:
- Computer Vision
- Geometric Modeling
- Shape Analysis
Background:
- Matching 3D objects to 2D images is challenging due to perspective distortions.
- Existing methods may struggle with complex curved surfaces.
Purpose of the Study:
- To develop a robust method for matching 3D objects with curved surfaces to 2D perspective views.
- To represent object shapes using curvature functions and Fourier transforms for efficient matching.
Main Methods:
- Storing 3D object models as characteristic closed space curves.
- Converting 2D perspective projections into spline representations.
- Deriving curvature functions from sampled splines.
- Utilizing the Fourier transform of curvature for shape representation.
- Employing the Levenberg-Marquardt algorithm for minimization-based matching.
Main Results:
- The proposed method enables accurate matching of 3D curved objects to their 2D projections.
- Shape representation via Fourier transform of curvature proves effective for distinguishing objects.
- The Levenberg-Marquardt algorithm efficiently solves the matching minimization problem.
Conclusions:
- This novel approach offers a reliable solution for 3D to 2D object matching, particularly for objects with curved surfaces.
- The method's reliance on curvature and Fourier analysis provides a unique and effective shape descriptor.
- The technique has potential applications in areas requiring 3D object recognition from 2D imagery.
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