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Published on: November 14, 2019
Robustness of shape descriptors to incomplete contour representations
1Institute of Mathematics and Computing Science, University of Groningen, The Netherlands. anarta@cs.rug.nl
IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 16, 2005
Summary
This study introduces the Incomplete Contour Recognition (ICR) test to evaluate shape recognition algorithms. The ICR test reveals that algorithms perform best with random contour loss and worst with occlusions, mimicking human vision.
Area of Science:
- Computer Vision
- Image Processing
- Pattern Recognition
Background:
- Human visual perception provides inspiration for developing robust shape recognition algorithms.
- Evaluating algorithm performance with incomplete contour data is crucial for real-world applications.
- Existing methods lack standardized evaluation for contour incompleteness.
Purpose of the Study:
- To propose a novel method for evaluating the robustness of contour-based shape recognition algorithms to contour incompleteness.
- To introduce the Incomplete Contour Recognition (ICR) test as a standardized evaluation framework.
- To compare the performance of shape context and distance multiset descriptors under contour incompleteness.
Main Methods:
- Developed the Incomplete Contour Recognition (ICR) test using complete contours as reference and incomplete contours as test data.
- Investigated three types of contour incompleteness: segment-wise deletion, occlusion, and random pixel depletion.
- Evaluated two contour-based shape recognition algorithms utilizing shape context and distance multiset local descriptors.
Main Results:
- Recognition performance consistently increases with the percentage of contour retained across all tested incompleteness types.
- Both algorithms demonstrated better performance with random pixel depletion and poorer performance with occluded contours.
- The distance multiset descriptor outperformed the shape context descriptor within the ICR test framework.
Conclusions:
- The proposed ICR test effectively evaluates the robustness of contour-based shape recognition algorithms to contour incompleteness.
- Algorithm performance degradation varies significantly with the type of contour incompleteness, with occlusion being the most challenging.
- The distance multiset descriptor offers superior robustness compared to shape context for incomplete contour recognition tasks.
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