Meaningful scales detection along digital contours for unsupervised local noise estimation

Area of Science:

• Computer Vision
• Digital Geometry
• Image Processing

Background:

• Automatic detection of noise and damage in digital contours is challenging due to the difficulty in distinguishing relevant information from perturbations.
• Accurate contour analysis is crucial for applications like image segmentation, geometric estimation, shape matching, and image editing.

Purpose of the Study:

• To propose an original strategy for automatically detecting relevant scales at which contour points should be analyzed.
• To enable the automatic identification and quantitative evaluation of noisy or damaged parts of digital contours.

Main Methods:

• The proposed strategy leverages theoretical results from asymptotic discrete geometry.
• It determines the relevant scales for analyzing each point on a digital contour.
• The method is parameter-free, except for a maximal observation scale, and is easy to implement.

Main Results:

• The approach successfully detects noisy or damaged contour segments.
• It provides a quantitative measure of the noise level.
• Demonstrated effectiveness across various datasets.

Conclusions:

• The developed method offers an effective solution for automatic noise detection in digital contours.
• It has direct applications in contour smoothing and geometric estimation, improving algorithms that previously required manual noise/scale parameter tuning.
• Highlights the pertinence of the proposed measure for digital shape analysis and reconstruction.