Paper
1 April 1992 Morphological gradients
Jean-Francois Rivest, Pierre Soille, Serge Beucher
Author Affiliations +
Proceedings Volume 1658, Nonlinear Image Processing III; (1992) https://doi.org/10.1117/12.58373
Event: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, 1992, San Jose, CA, United States
Abstract
Object boundaries are generally characterized by grey level intensity transitions. In order to detect these variations, gradient masks are widely used, and this paper surveys the morphological framework of gradient operators. Morphological gradients are based on the difference between extensive and anti-extensive transformations. For instance dilations and erosions with structuring elements containing their origin belong to this class of transformations. Generally, these gradients are used in segmentation applications with edge finders such as sequential searches, thresholdings or the watershed transformation. The robustness of this latter transformation allows more tolerances for the construction of a gradient operator. After a short introduction to gradients in digital images the gradients available in mathematical morphology are presented: Beucher, internal and external, thick, regularized, directional, and thinning/thickening gradients. Applicability and performance of each gradient are briefly evaluated, followed by a generalization of the morphological framework of gradient operators to other digital sources.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jean-Francois Rivest, Pierre Soille, and Serge Beucher "Morphological gradients", Proc. SPIE 1658, Nonlinear Image Processing III, (1 April 1992); https://doi.org/10.1117/12.58373
Lens.org Logo
CITATIONS
Cited by 6 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image segmentation

Nonlinear image processing

Edge detection

Sensors

3D image processing

Mathematical morphology

Roads

Back to Top