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Updated: Aug 14, 2026
05:39
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
Published on: March 18, 2019
Affine-permutation invariance of 2-D shapes
Victor H S Ha1, José M F Moura
1Carnegie Mellon University, Pittsburgh, PA 15213-3890, USA. v.ha@samsung.com
Summary
This study introduces intrinsic shape, a new invariant for recognizing objects despite distortions like scaling and rotation. The BLAISER algorithm recovers this intrinsic shape, enabling robust object identification from transformed images.
Area of Science:
- Computer Vision
- Image Processing
- Geometric Invariants
Background:
- Object recognition is challenged by shape distortions (translation, rotation, scaling, etc.).
- Permutation transformations further complicate relating different views of the same object.
- Existing methods struggle with combined affine and permutation distortions.
Purpose of the Study:
- To introduce a novel shape invariant, the intrinsic shape.
- To develop an algorithm (BLAISER) for recovering the intrinsic shape.
- To enable robust object identification invariant to affine-permutation distortions.
Main Methods:
- Introduced the concept of intrinsic shape as a unique descriptor.
- Developed the BLAISER algorithm to compute intrinsic shape from distorted images.
- Utilized low-order moments for robustness to noise and errors.
Main Results:
- The intrinsic shape is invariant to affine and permutation transformations.
- BLAISER successfully recovers the intrinsic shape without prior knowledge of distortions.
- The algorithm demonstrates robustness to noise and errors.
Conclusions:
- Intrinsic shape provides a reliable representation for distorted objects.
- BLAISER offers a powerful tool for object recognition in challenging imaging conditions.
- The method advances the field of shape analysis and invariant representation.
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