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Robust real-time segmentation of images and videos using a smooth-spline snake-based algorithm
Frederic Precioso1, Michel Barlaud, Thierry Blu
1Laboratoire 13S--UPRES-A 6070 CNRS, Université de Nice, F-06903 Sophia-Antipolis, France. frederic.precioso@i3s.unice.fr
Summary
This study enhances active contour segmentation for videos by using smoothing splines. This approach improves image segmentation quality and maintains real-time performance, even with noisy data.
Area of Science:
- Computer Vision
- Image Processing
- Computational Geometry
Background:
- Region-based active contours with level sets are effective for video segmentation but computationally expensive.
- Parametric active contours using B-Spline interpolation reduce computational cost but are sensitive to noise.
Purpose of the Study:
- To develop a robust and computationally efficient active contour method for video segmentation.
- To improve segmentation quality in the presence of noise without increasing computational load.
Main Methods:
- Relaxing rigid interpolation constraints by employing smoothing splines.
- Trading a controllable amount of interpolation error for a smoother spline curve.
- Applying the method to natural image sequences for experimental validation.
Main Results:
- The proposed smoothing spline method achieves higher quality segmentation results compared to existing methods.
- The technique maintains real-time processing capabilities for moving object segmentation.
- Robustness to noise is significantly improved without additional computational expense.
Conclusions:
- Smoothing splines offer a flexible and effective way to enhance active contour segmentation.
- This method preserves real-time performance while improving segmentation accuracy and noise robustness.
- The approach is suitable for efficient and high-quality video segmentation applications.