Contour detection and completion for inpainting and segmentation based on topological gradient and fast marching...

Area of Science:

• Image Processing
• Computer Vision
• Computational Mathematics

Background:

• Standard gradient methods offer limited global image analysis for edge detection.
• Image processing tasks like inpainting and segmentation necessitate continuous contours for accurate results.
• Existing methods may struggle with identifying and connecting main image edges effectively.

Purpose of the Study:

• To develop a hybrid algorithm for detecting connected contours by integrating topological gradient and minimal path methods.
• To leverage the global analysis capabilities of topological gradients with the pathfinding efficiency of minimal path algorithms.
• To address the need for accurate and continuous contours in image processing applications.

Main Methods:

• Utilizing the topological gradient for a more global image analysis and main edge identification.
• Employing a variant of the minimal path method to find connected contours.
• Integrating the fast marching algorithm to compute minimal paths within the topological gradient image.

Main Results:

• The coupled algorithm effectively identifies main image edges using topological gradients.
• The fast marching algorithm successfully finds minimal paths, ensuring contour connectivity.
• The hybrid approach delivers accurate and connected contours suitable for various image processing tasks.

Conclusions:

• The proposed hybrid algorithm offers a robust solution for generating accurate, connected contours.
• This method enhances image processing capabilities in applications such as inpainting and segmentation.
• The combination of topological gradient and minimal path methods represents a significant advancement in contour detection.