A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing
Digital Image Processing
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Image filtering using morphological amoebas
Image and Vision Computing
Theoretical Foundations of Spatially-Variant Mathematical Morphology Part I: Binary Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical Foundations of Spatially-Variant Mathematical Morphology Part II: Gray-Level Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Edge detection is a significant step in image processing. Morphological edge detectors developed until now used a fixed structuring element (SE) on all the image pixels; however, they cannot consider the local features of an image due to the fixed SE and we should choose an appropriate SE by lots of experiments. In this paper, new morphological edge detectors using amoebas, dynamic structuring elements which adapt their shapes to image contours, are proposed. The experimental results show that amoeba-based edge detectors have better performance than corresponding classic edge detectors. The proposed methods have less sensitivity to noise while detecting more details of image than other morphological edge detectors with a fixed SE.