Edge detection based on morphological amoebas

TL;DR

This paper introduces a novel edge detection method using morphological amoebas that adapt their shape to image contours, outperforming traditional morphological edge detectors in both qualitative and quantitative evaluations.

Contribution

The paper proposes a new amoeba-based edge detection technique that dynamically adjusts shape for improved accuracy over classic methods.

Findings

Amoeba-based method outperforms classic morphological edge detectors.

The technique is effective in both qualitative and quantitative assessments.

The method adapts to image contour variations for better edge detection.

Abstract

Detecting the edges of objects within images is critical for quality image processing. We present an edge-detecting technique that uses morphological amoebas that adjust their shape based on variation in image contours. We evaluate the method both quantitatively and qualitatively for edge detection of images, and compare it to classic morphological methods. Our amoeba-based edge-detection system performed better than the classic edge detectors.