Structuring element

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

Decomposition based on Object of Convex Shapes Using Poisson Equation

Abstract: This paper proposes a novel procedure that uses a combination of overlapped basic convex shapes to decompose 2D silhouette image. A basic convex shape is used here as a structuring element to give a meaningful interpretation to 2D images. Poisson equation is utilized to obtain the basic shapes for either the whole image or a partial region or segment of an image. The reconstruction procedure is used to combine the basic convex shapes to generate the original shape. The decomposition process involves a merging stage, filtering stage and finalized by compromising stage. The merging procedure is based on solving Poisson’s equation for two regions satisfying the same symmetrical conditions which leads to finding equivalencies between basic shapes that need to be merged.We implemented and tested our novel algorithm using 2D silhouette images. The test results showed that the proposed algorithm lead to an efficient shape decomposition procedure that transforms any shape into a simpler basic convex shapes.

Decomposition of grey-scale morphological structuring elements in hardware

Abstract: Morphological image processing machines are not capable of handling large-size structuring elements. A new architecture for fast execution of the erosion/dilation operations in an up to 9 × 9-pixel, arbitrarily shaped, image window through decomposition of grey-scale morphological structuring element into 3× 3-pixel sub-domains is presented in this paper. The proposed hardware structure has been also implemented in VLSI and its throughput rate is 10 Mbytes/sec.

Adaptive multiparameter openings

Abstract: Adaptation of an opening possessing a multiparameter structuring element is studied in the context of Markov chains by treating the multiple parameters as a vector r defining the state of the system and considering the operativefilter Ar to be opening by reconstruction. Adaptation of Ar (transition of r) is in accordance to whether or not Ar conectly or incorrectly passes signal and noise grains sampled from the image. Signal and noise are modeled as unions ofrandomly parameterized and randomly translated primary grains. Transition probabilities are discussed fortwo adaptation protocols and the state-probability increment equations are deduced from the appropriate Chapman- Kolmogorov equations. Adaptation convergence is characterized by the steady-state distributions of the Markov chains and these are computed numerically. 1. INTRODUCTION Analytic derivation of optimal binary openings was first treated for a restricted model in which grains were assumedto be disjoint homothetics of some basic generating shape that also served as the opening structuring element [1]. A

3‐D spatiotemporal subband decomposition of image sequences by mathematical morphology

Abstract: An efficient 3‐D spatiotemporal image sequence decomposition method using mathematical morphology is described in this paper. The method can be used to decompose the spectrum of the input signal into 8 and 4 spatiotemporal subband images. It does this using two different sets of structuring elements. After decomposition, each band image can be decimated and coded for data transmission. This subband pyramid scheme preserves the number of pixels which existed in the original image, has an efficient hierarchical data structure, and allows parallel implementation. Therefore, this scheme has great potential for High Definition Television (HDTV) coding, multimedium video compression, etc. As regards filtering, the unique advantages of morphology over the linear filtering approach are: 1) it utilizes direct geometric interpretations; and 2) it is simple and efficient when used in conjunction with parallel/pipelining hardware. Some image sequence examples are given to show the effectiveness of this approach.