Morphological image processing is well known as an efficient methodology for image processing and computer vision. With the wide use of color in many areas, the interest on the color perception and processing has been growing rapidly. Many models have been proposed to extend morphological operators to the field of color images, dealing with some new problems not present previously in the binary...

A new set of non linear signal and image processing operators is presented. Their definition is based on the introduction of the statistical properties of Bayesian reconstruction in soft morphological operators. Statistical soft operators represent a trade-off between the noise cleaning properties of statistical morphology and the shape preservation properties of soft morphology. The main chara...

The author introduces a new technique to construct generic morphological operators quickly and automatically using input-output pairs of images as training samples. This process can be modeled as generalized PAC (Probably Approximately Correct) learning. He also introduces a new representation for morphological operators, much more efficient than the traditional one. Introduction: Mathematical ...

Mathematical Morphology (MM) studies the representation of image operators in terms of some families of simple operators, called the elementary operators of MM. A drawback of this approach is that the representation of some operators is not efficient, since it uses a large number of elementary operators. Fast algorithms are the solution found by some authors to implement image operators that ha...

Morphological filtering is a useful technique for processing and analysis of binary and grayscale images. The extension of morphological techniques to color images is not a straightforward task, because this extension stems in the multivariate ordering problem. Since the multivariate ordering is ambiguous, the existing approaches have used the known vector ordering schemes for the color orderin...

In gray level images discontinuities in image intensity profiles is called edges. Detection of these discontinuities is essential steps in many machine vision tasks such as object recognition, motion analysis etc. In this paper, various morphological operators are used on MRI images and a new algorithm is applied to give better result. Keywords-Morphological operators, MRI images and threshold ...

Based on the observation that rough sets and mathematical morphology are both using dual operators sharing similar properties, we investigate more closely the links existing between both the domains. We establish the equivalence between some morphological operators and rough sets de"ned from either a relation, or a pair of dual operators or a neighborhood system. Then we suggest some extensions...

We present a new tree-based framework for producing self-dual morphological operators. For any given tree representation of images, one can associate a complete inf-semilattice (CISL) in the corresponding tree-representation domain, where the operators can then be derived. We also present a particular case of this general framework, involving a new tree representation, the ExtremaWatershed Tree...

Rank order morphological operators have been designed to better cope with shape variations and noise than the corresponding operators with plain structuring elements. In this paper, starting from a brief historical overview of rank order "lters and erosions/dilations in digital image processing, rank order based morphological operators are surveyed. We also highlight the relations between these...

Edge detection plays a important role in image analysis. It is used to detect the different edges in the images. By using the combination of different morphological operators in the proposed method like erosion, dilation with the different edge operators like Robert ,prewitt, sobel, log and canny to find the edges by using the structure element. The fine edges in different direction can be dete...