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Updated: Mar 6, 2026
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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
Published on: January 18, 2020
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Joint A Contrario Ellipse and Line Detection
Summary
This study introduces a novel line segment and elliptical arc detector that minimizes false detections in images without parameter tuning. The detector uses a statistical criterion for accurate model validation and selection, improving performance on diverse image types.
Area of Science:
- Computer Vision
- Image Processing
- Pattern Recognition
Background:
- Accurate detection of geometric shapes like line segments and elliptical arcs is crucial for image analysis.
- Existing methods often require parameter tuning and can produce numerous false detections.
Purpose of the Study:
- To develop a robust line segment and elliptical arc detector with reduced false detections.
- To implement a parameter-free approach for model validation and selection.
Main Methods:
- A novel statistical criterion based on the a contrario theory is proposed.
- The criterion is used for both validating the presence of shapes and selecting the best model (line segment or elliptical arc).
- The detector operates on grey-scale image regions without requiring any parameter adjustments.
Main Results:
- The proposed detector significantly reduces false detections across various image types.
- Experimental results demonstrate superior performance compared to state-of-the-art detectors.
- The approach is effective on both synthetic and real-world images.
Conclusions:
- The a contrario-based detector offers a reliable and parameter-free solution for line segment and elliptical arc detection.
- This method enhances the accuracy and efficiency of image analysis tasks.
- The approach shows strong potential for applications in computer vision and image processing.
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