Structuring element

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

An Image Edge Detection Method Based on Mathematical Morphology

Abstract: We present two kinds of improved morphological edge detector, according to the characteristics of the classical morphological operators and the disadvantages of the single structuring elements selected. The structure of the first improved operator considers multi-structuring elements and omni-directional structuring elements, and characteristics of dilation, erosion, opening and closing, The second improved operator uses the different structural element on the basis of the first improved operator, which is characterized by multi-structure, multi-scale and multi-directional. The final experimental results show that, comparing with the classical morphological operators and traditional edge detectors, the edge detection effects and denoising ability of improved operators are more superior, and they are practical strongly, and can be used widely in the subsequent image processing.

Effectiveness of morphological reconstruction operators in change detection for remote sensing images

Abstract: Change detection (CD) in multi-temporal images aims at quantifying the temporal effects or changes in remote sensing images taken at different times of the same area of Earth With huge constellations of satellites setup by various countries, monitoring the Earth's surface for changes helps us understand and respond to various natural phenomenon affecting the Earth's atmosphere In this paper, we have used texture features along with the spectral features for CD The texture features have been extracted after applying three different morphological reconstruction operators, namely, opening by reconstruction, closing by reconstruction and opening of closing by reconstruction Also flat and non-flat structuring elements (SE) have been used for applying morphological reconstruction The paper compares the impact of different types of morphological operators on the accuracy of the CD It also presents the comparison between the effectiveness of flat and non-flat SEs on the accuracy

Binary image representation and coding by a double-recursive morphological algorithm

Abstract: This paper presents a recursive morphological operation which can be advantageously substituted to classical erosion with an application for image coding. A structuring element is translated over binary image following four scanning modes. The result of the operation, as for classical erosion, corresponds to the centers of the structuring elements which are included in the image objects, but our operation authorizes only a minimal overlap between the translated structuring elements. This minimal overlap needs to take into account previous erosions, so the operation is recursive. It results in a subset of the erosion, containing fewer points than the erosion, but gives a good approximation to image objects. In a second step, operation is extended with a second recursivity level corresponding to structuring element size variation. This new double-recursive morphological algorithm describes an image object, from coarser structures to finer details, by the loci and sizes of the translated structuring elements which are included in the object. This algorithm provides a minimal redundancy shape representation. To evaluate this algorithm in the context of binary image coding, we propose two different techniques for encoding representative points issued from our shape decomposition algorithm. Comparative experimental results with two classical methods (Modified Huffman RLC and TUH code) and also a morphological method (discrete skeleton), expressed in terms of compression ratio, show the good performances of our approach.

A Separating Algorithm for Overlapping Red Blood Cell Images

Abstract: Overlapping appears frequently is all images The algorithm based on the knowledge that red blood cells are disk-shaped Granulometry is employed to estimate cell’s radius R Every contour point corresponds to center point of a circle with radius R estimated with the information of radius and chain code, which will assemble central areas of every single cell Then, morphological dilation with a disk-shaped structuring element of radius R is applied to every central area The intersection of the result of dilation operation and the original binary image is used to estimate the shape of every single cell Experiments show the algorithm can obtain acceptable separating result It can also be used for images of other kind of disk-shaped cells and granules

Multiscale segmentation of oil slick in sar images based on morphological pyramid

Abstract: We propose a new approach based on the Marangoni theory, particularly a dampening of the wave spectra energy. The observation is decomposed into multiscale analysis using a pseudo morphological pyramid, named here as a alternating contextual filter, to improve the detection of local variations of the wave spectrum. The morphological thick gradient contrast is first performed with a varying structuring element. The filtering is balanced by the low pass image. The residue image gives information about the surface energy spectrum in relation to the dispersion equation. Then images generated are merged with the help of fuzzy c-mean algorithm to achieve the segmentation process. The method is tested on ERS2 SAR and ENVISAT ASAR Images. The obtained results are promising and show an improvement of the oil slick detection. 1. INTRODUCTION ERS Synthetic Aperture Radar (SAR) images had proved their interest in large scale detection and monitoring of pollution on ocean surface. Advanced SAR (ASAR) images greatly offers new possibilities of applications [1]. Up until now, several methods based on intensity images have been used to measure signatures of oil slick by a spaceborne mono-frequency Synthetic Aperture Radar (SAR) [2] [3]. Among these methods, the Multiscale Oil Slick Segmentation (MOSS) with Markov Chain Model (MCM) [4] has been based on the behaviour of wave spectra [5]. It has proved multiscale analysis to be an original approach to explain the effects of slick on sea, to characterize ocean surface polluted, particularly the dampening of the wave spectra energy, and then to classify oil slicks pixels. In this previous work, the multiscale analysis has been implemented by wavelet packets decomposition for frequential texture characterization. But, the local variation of the wave spectrum required being characterise structurally [3]. The multiscale contrast thick gradients resulting from the morphological pyramid [6], with a varying structuring element, are non linear approaches suitable to answer these two points of view. A conflict area (beyond the possible classes) has been appeared too in the images detection result of the MCM. To raise this ambiguity, the locations will be refining with fuzzy model algorithm. The paper is organized as follows. Section 2 recalls the main concept of ocean modelling. Then, the method (section 3) operates in two mains steps: the multiscale decomposition by morphological pyramid based on contextual filtering and the multi spectral fuzzy classification performed to merge additional sub-bands generated. Experimental results are illustrated in section 4, at the same time, on ENVISAT ASAR and ERS2 SAR images, to evaluate these two radar results. Conclusions are reported in section 5.