Paper
1 May 1994 Case studies of morphological top-hat optimization
Edward R. Dougherty, Dongming Zhao
Author Affiliations +
Proceedings Volume 2180, Nonlinear Image Processing V; (1994) https://doi.org/10.1117/12.172561
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States
Abstract
This paper presents some optimal approaches to morphological top-hat transform. When using top-hat transform, size estimation of structuring elements is critical in performing tasks such as object segmentation. One typical example is the moving ball algorithm. Since the objects of particular interest possess various size measures, an optimal procedure for selecting structuring functions appears appropriate for the purpose of adaptive thresholding. An optimization design can result in a minimum error according to certain rules in error estimation. In this paper, three cases are considered. The first is the case where the cylindrical type of structuring elements and objects are investigated. The second is on a conical model where a cone is modeled as to optimize top-hat transform. The third case presents optimal algorithm via threshold area or umbra based on a cylindrical model. As is often typical in random geometric modeling, optimization leads very quickly to quite complicated mathematical expressions involving the distributions of the parameters.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Edward R. Dougherty and Dongming Zhao "Case studies of morphological top-hat optimization", Proc. SPIE 2180, Nonlinear Image Processing V, (1 May 1994); https://doi.org/10.1117/12.172561
Lens.org Logo
CITATIONS
Cited by 5 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Mathematical modeling

Image segmentation

Chemical elements

Optimization (mathematics)

Binary data

Nonlinear image processing

Detection and tracking algorithms

Back to Top