Arbitrary Structuring Elements Research Articles

Automatic generation of adaptive structuring elements for road identification in VHR images

Road extraction from very high resolution remotely sensed images is crucial in many urban applications. Acquiring automatically up-to-date and accurate information about roads is significant for various intelligent applications such as smart vehicle navigation, planning urban areas, roads monitoring and traffic management for intelligent transportation systems, and leading proper military operations. All possible knowledge about roads properties must be incorporated in designing intelligent systems that interpret and decide with high precision the existence of roads in remote sensing images. Various extraction techniques rely on mathematical morphology (MM) that detects desired road structures through a sliding standard and empirically chosen structuring element (SE) over the input image. In this paper, we design an intelligent process that not only combines spectral and spatial properties of roads but also impacts significantly the flexibility in retrieving spatial information. Indeed, we propose an adaptive algorithm that supplies tailored and most adequate arbitrary structuring elements for every image at hand. It has the significant impact of providing flexibility since every arbitrary generated SE is exclusively dedicated to the processed image. The processing consists of two major steps: a) we use the particle swarm optimization algorithm to look for the adaptive SEs; b) we introduce a priori knowledge based on human visual interpretation of roads characteristics and define some spatial indices to refine the results. We evaluated our method over many remotely sensed images; accuracy results show that the proposed method outperforms standard approaches which are limited to utilize only empirically chosen and standard SEs.

Fast computation of morphological operations with arbitrary structuring elements

This paper presents a general algorithm that performs basic mathematical morphology operations, like erosions and openings, with any arbitrary shaped structuring element in an efficient way. It is shown that our algorithm has a lower or equal complexity but better computing time than all comparable known methods.

Some sequential algorithms for a generalized distance transformation based on Minkowski operations

A generalized distance transformation (GDT) of binary images and the related medial axis transformation (MAT) are discussed. These transformations are defined in a discrete space of arbitrary dimension and arbitrary grids. The GDT is based on successive morphological operations using alternatively N arbitrary structuring elements: N is called the period of the GDT. The GDT differs from the classical distance transformations based on a point-to-point distance. However, the well-known chessboard, city-block, and hexagonal distance transformations are special cases of the one-period GDT, whereas the octagonal distance transformation is a special case of the two-period GDT. In this paper, both one- and two-period GDTs are discussed. Different sequential algorithms are proposed for computing such GDTs. These algorithms need a maximum of two scannings of the image. The computation of the MAT is also discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Morphological transformations of binary images with arbitrary structuring elements

This paper proposes a versatile algorithm for computing dilations and erosions of digital binary pictures with structuring elements of arbitrary size and shape. The first step consists in a tracking of the contours of the image under study and their encoding as loops. In a second step, the structuring element is efficiently propagated along the different loops thanks to its encoding in a customized data structure. The algorithm turns out to be extremely fast whatever the utilized elements. Moreover, even when simple shapes like squares or hexagons are considered, it is faster than most existing implementations. Finally, the present paper provides some examples of application, which highlight the practical interest of arbitrary structuring elements in the field of mathematical morphology.

Erosion and dilation of binary images by arbitrary structuring elements using interval coding

Interval coding of binary images provides a representation in which the mathematical morphology operations of dilation and erosion by an arbitrary structuring element can be naturally and efficiently implemented on a serial computer. Conventional 4- or 8-neighbour dilation and erosion are then only special cases. Euclidean mapping is investigated as an example.