Structuring element

About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.

Land use classification from vhr aerial images using invariant colour components and texture

Abstract: . Very high resolution (VHR) aerial images can provide detailed analysis about landscape and environment; nowadays, thanks to the rapid growing airborne data acquisition technology an increasing number of high resolution datasets are freely available. In a VHR image the essential information is contained in the red-green-blue colour components (RGB) and in the texture, therefore a preliminary step in image analysis concerns the classification in order to detect pixels having similar characteristics and to group them in distinct classes. Common land use classification approaches use colour at a first stage, followed by texture analysis, particularly for the evaluation of landscape patterns. Unfortunately RGB-based classifications are significantly influenced by image setting, as contrast, saturation, and brightness, and by the presence of shadows in the scene. The classification methods analysed in this work aim to mitigate these effects. The procedures developed considered the use of invariant colour components, image resampling, and the evaluation of a RGB texture parameter for various increasing sizes of a structuring element. To identify the most efficient solution, the classification vectors obtained were then processed by a K-means unsupervised classifier using different metrics, and the results were compared with respect to corresponding user supervised classifications. The experiments performed and discussed in the paper let us evaluate the effective contribution of texture information, and compare the most suitable vector components and metrics for automatic classification of very high resolution RGB aerial images.

Approaches to decompose flat structuring element for fast overlapping search morphological algorithm

Abstract: The fast morphological algorithm, overlapping search (OS) algorithm, recently proposed by Lam and Li (1998), can only be applied to a flat structuring element (FSE) whose 1D Euler-Poincare constants, N/sup (1)/(x) and N/sup (1)/(y), at any x or y coordinate are equal to 1. Thus, an arbitrarily shaped structuring element must be decomposed to a set of constrained components before employing the fast algorithm. In the paper, a decomposition algorithm, based on line-scanning process, and an improved algorithm, based on conditionally maximal convex polygon (CMCP), are proposed to generalize the use of the overlapping search morphological algorithm for any arbitrary flat structuring element.

Structuring elements selection in morphology filter

Abstract: As a kind of non-linear filter,morphology filter is applied widely in image processing such as object extraction and noise removing etc.The paper proposes a new adaptive approach for selecting the scale of structuring element in binary images,which depends on the research of the element decomposition and morphology filter.The experiments show that the method can select the scale of structuring element exactly,and at the same time remove the noise completely and effectively.

Automatic selection of structuring element for bengali numeral recognition

Abstract: How to select a structuring element for a given task is one of the most frequently asked questions in morphology. The present work tries to find a solution for a restricted class of problems, in the domain of shape classification. In this work an algorithm that extracts distinctive structure of each of a given set of objects, which can be used as the structuring elements for object classification system employing the hit-and-miss transformation, is proposed. The proposed algorithm is based on a new measure of local shape property. The method is used to develop a system for Bengali numeral recognition.

Parallel Particle Swarm Optimization Algorithm for Classification of Very High Resolution Images Based on Mathematical Morphology

Abstract: This paper investigates parallel particle swarm optimization (parallel PSO) and mathematical morphology (MM) for classification of very high resolution (VHR) remote sensing images. The proper extraction of spatial features depends on the shape and size of the structuring element (SE) used to probe the image. However, finding the best shape and size of the SE for classification of urban remote sensing imagery is a challenging research topic, because the formats of the features may become more and more complex. For this purpose, this paper proposes a new adaptive algorithm based on the combination of parallel PSO with MM for the construction of adaptive SEs. Such that the shapes and sizes of the constructed SEs change adaptively according to the spatial features of the image and the considered classification task. As a result, the application of parallel PSO reduces the execution time compared to serial PSO, and makes the proposed algorithm can adopt several SEs simultaneously for image classification without complicating the main PSO algorithm. Finally, an experimental example is conducted to show the effectiveness of the proposed algorithms.