Affiliations: [a] Federal University of S. Carlos, S. Carlos, SP, Brazil | [b] Federal University of Rio Grande do Norte, Natal, RN, Brazil | [c] FACCAMP, C. L., Paulista, SP, Brazil
Correspondence:
[*]
Corresponding author: E.C. Pedrino, Federal University of S. Carlos, S. Carlos, SP, Brazil. E-mail:[email protected]
Abstract: Mathematical morphology (MM) is a popular formalism
largely used for image processing. Of particular relevance in MM-based
operations is the structuring element (SE). In an image processing environment
SE defines which pixel values, in the input image, to include in the
calculation of the output value. Most MM-based image processing environments
employ limited size SEs which prevents their use in tasks requiring larger SEs. This
paper proposes a computer-based method for optimizing the decomposition of
SEs, in binary image related tasks, that employs binary MM, which automatically
transforms an original SE into a corresponding sequence of 3 × 3 SEs. The
decomposition operation reduces the complexity of the morphological
operations and has been implemented as a genetic algorithm (GA) based
process, that searches for the best sequence of smaller structuring
elements, using one dilation and four union operations, for the
decomposition of each large-sized structuring element. By using a GA with a
fixed-length chromosome as well as a fixed number of dilation and union
operations, the method has a simple and fixed structure, which makes it a
convenient choice for hardware implementations. Its performance, based on
six images already used in the literature by other well-established method,
has shown to be competitive.
