Showing papers on "Structuring element published in 2020"

Morphological Filter Detector for Image Forensics Applications

Abstract: Mathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and gray level documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.

Mathematical morphology directly applied to point cloud data

Abstract: Many of the point cloud processing techniques have their origin in image processing. But mathematical morphology, despite being one of the most used image processing techniques, has not yet been clearly adapted to point clouds. The aim of this work is to design the basic operations of mathematical morphology applicable to 3D point cloud data, without the need to transform point clouds to 2D or 3D images and avoiding the associated problems of resolution loss and orientation restrictions. The object shapes in images, based on pixel values, are assumed to be the existence or absence of points, therefore, morphological dilation and erosion operations are focused on the addition and removal of points according to the structuring element. The structuring element, in turn, is defined as a point cloud with characteristics of shape, size, orientation, point density, and one reference point. The designed method has been tested on point clouds artificially generated, acquired from real case studies, and the Stanford bunny model. The results show a robust behaviour against point density variations and consistent with image processing equivalent. The proposed method is easy and fast to implement, although the selection of a correct structuring element requires previous knowledge about the problem and the input point cloud. Besides, the proposed method solves well-known point cloud processing problems such as object detection, segmentation, and gap filling.

Enhancement of Brain MR-T1/T2 Images Using Mathematical Morphology

Abstract: Brain tumor is a life-threatening disease with a fast growth rate, which makes its detection a critical task. However, low contrast and high noise content in brain MR images hamper the screening of tumor. Enhancement is therefore done to improve the perceivable features of these images. This paper presents an improved enhancement technique of brain MR-T1/T2 images by employing morphological filters. In this method, a disk-shaped flat structuring element along with top-hat and bottom-hat morphological operators is used. The performance of the filter is validated by incrementing values of contrast improvement index (CII) and peak signal-to-noise ratio (PSNR) parameters indicating a successful enhancement without noise amplification.

Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering

Abstract: Although automatic fuzzy clustering framework (AFCF) based on improved density peak clustering is able to achieve automatic and efficient image segmentation, the framework suffers from two problems. The first one is that the adaptive morphological reconstruction (AMR) employed by the AFCF is easily influenced by the initial structuring element. The second one is that the improved density peak clustering using a density balance strategy is complex for finding potential clustering centers. To address these two problems, we propose a fast and automatic image segmentation algorithm using superpixel-based graph clustering (FAS-SGC). The proposed algorithm has two major contributions. First, the AMR based on regional minimum removal (AMR-RMR) is presented to improve the superpixel result generated by the AMR. The binary morphological reconstruction is performed on a regional minimum image, which overcomes the problem that the initial structuring element of the AMR is chosen empirically, since the geometrical information of images is effectively explored and utilized. Second, we use an eigenvalue gradient clustering (EGC) instead of improved density peak (DP) algorithms to obtain potential clustering centers, since the EGC is faster and requires fewer parameters than the DP algorithm. Experiments show that the proposed algorithm is able to achieve automatic image segmentation, providing better segmentation results while requiring less execution time than other state-of-the-art algorithms.

Multilingual Scene Text Detection Using Gradient Morphology

Abstract: Text detection in natural scene images is an interesting problem in the field of information retrieval. Several methods have been proposed over the past few decades for scene text detection. Howeve...

Fast Implementation of Morphological Filtering Using ARM NEON Extension.

Abstract: In this paper we consider speedup potential of morphological image filtering on ARM processors. Morphological operations are widely used in image analysis and recognition and their speedup in some cases can significantly reduce overall execution time of recognition. More specifically, we propose fast implementation of erosion and dilation using ARM SIMD extension NEON. These operations with the rectangular structuring element are separable. They were implemented using the advantages of separability as sequential horizontal and vertical passes. Each pass was implemented using van Herk/Gil-Werman algorithm for large windows and low-constant linear complexity algorithm for small windows. Final implementation was improved with SIMD and used a combination of these methods. We also considered fast transpose implementation of 8x8 and 16x16 matrices using ARM NEON to get additional computational gain for morphological operations. Experiments showed 3 times efficiency increase for final implementation of erosion and dilation compared to van Herk/Gil-Werman algorithm without SIMD, 5.7 times speedup for 8x8 matrix transpose and 12 times speedup for 16x16 matrix transpose compared to transpose without SIMD.

Channel boundary detection using a 3D morphological filter and adaptive ellipsoidal structuring element

Abstract: Channels are one exploratory object in the oil and gas industry, and detailed channel studies can help identify the sedimentation processes in an area. In addition, channels are a drilling hazard i...

An Evaluation of Contrast Enhancement of Brain MR Images Using Morphological Filters

Abstract: Tumor is the uncontrollable growth of abnormal cells in the brain which can be screened using magnetic resonance imaging (MRI). But, MRI is prone to poor contrast and noise during acquisition. This might affect the visibility of the tumor in the image which makes contrast enhancement an essential part of MR image analysis for tumor detection. In this method, a disk-shaped flat structuring element is applied with morphological operators consisting of bottom-hat, dilation and erosion for the purpose of noise controlled enhancement of MRI tumors. The outcomes of the proposed method are validated by image fidelity assessment parameters like: contrast improvement index (CII) and peak signal-to-noise ratio (PSNR).

Mathematical morphology approach to internal defect analysis of A356 aluminum alloy wheel hubs

Abstract: A356 aluminum alloy is a material widely used in the production of automobile wheels. Internal defects such as gas holes and shrinkage cavities are likely to develop in the process of low pressure casting. X-ray images of the hub are able to provide some information on such defects. This paper proposes a defect extraction method which is built on mathematical morphology. It involves three operations, i.e., the top-hat transform, the top-hat reconstruction transform and the dilation reconstruction. A larger square structuring element and a small threshold are used firstly to obtain all potential defect areas of the hub. A structuring element of a suitable size are applied to different defect areas in subsequent extraction. A new threshold is then decided to get the final defect extraction results. The experimental results show that the above defect extraction method not only works on X-ray hub images, but is robust against the interference caused by noises and hub geometry, and hence can potentially be extensively applied to X-ray detection of hubs.

A Novel Methodology for the Determination of Impulse Response Coefficients Applied to Transmission Line Protection Relays

Abstract: Impulse Response Coefficients (IRC) of digital filters is an imperative step in the development of transmission line protection relay algorithms. Traditionally, Fourier-based filters are used in real applications, where IRC are fixed values of sine and cosine functions with a data window of one or more cycles. Based on state-of-the-art, Mother Wavelet coefficients used in Multiresolution Analysis, and Structuring Element coefficients used in Mathematical Morphology are usually proposed to develop protection algorithms. However, the proper choice of these coefficients is based on empirical process of trial and error. This paper proposes a novel methodology for optimally determining coefficients that depend on the waveform structure analyzed, which is determined using variance as the metric. Assessment of methodology for three case studies considering requirements of relay manufactures (response time, design, harmonic attenuation and other) is presented. The first assessment is to extract coefficients useful for distinguishing among non-fault conditions, harmonics, and arcing faults. The second one is to extract coefficients to filter harmonic components. The assessment is carried out considering different data windows and sampling rates. Test results highlight the efficiency of the model to determine specific coefficients for each case study analyzed. Interestingly, results also showed that the discovered coefficients can be used in another filtering technique. Thus, the third case study involves developing two fault classifiers, which are developed using mathematical morphology where the structuring elements used correspond to the coefficient vectors determined through the proposed methodology. There is a notable paucity of scientific literature focusing on this topic. Therefore, there are several important areas where this study makes an original contribution regarding protection relays.

Construction Algorithm for Adaptive Morphological Structuring Elements Based on the Neighborhood Gray Difference Changing Vector Field and Relative Density

Abstract: Structuring elements of fixed shape and size are used in most conventional mathematical morphology operations, which makes the border of image targets shift, produces new image artifacts and loses small image objects due to the diversity and complexity of the image targets. In this paper, a new construction algorithm for adaptive structuring elements is proposed based on the neighborhood gray difference changing vector field and relative density. The proposed structuring element is able to adaptively change shape according to the gray and edge characteristics of an image. This algorithm involves first incorporating the gray difference changing vector field to smooth the local image region and make the gray level within the image target more uniform and then defining a border degree function based on relative density to determine whether the center pixel of the local image region is a border pixel. The adaptive structuring element is composed of all the strong border pixels found in a local image region. Dilation and erosion operations and other derivative operations are proposed with this new adaptive structuring element based on conventional morphology operation principles. The experimental results show that this proposed algorithm is able to effectively suppress the shifting effect of the image target borders while accurately locating the border of the image target region. Additionally, other effective image information is retained and image distortion is reduced while weakening the image details.

Operators which are translation invariant

Abstract: The notion of translation-invariant operators is described and explained with reference to binary images. Erosion and dilation, opening and closing are discussed at length. Grey-scale morphology is introduced and the use of the umbra is presented. T-operators and their representation are described.

Structuring Element for Secure Reversible Watermarking

Abstract: The ability to embed data into image with reversibility would allow host image can be restored from modification in embedding task. Structuring elements provided an information-rich link of host image before and after embedding data that could be important in the development of new way of exploring secure competence. We show how a structuring element can be designed for a gray images and used in a filter to allocate positions to hide securely secret data. The pixel locations are then collected to offer capacity of hiding data for the host image. We also demonstrate a connection between the structuring elements and the capacity to keep certainly high detecting complexity.

Segmentation of Blood Vessels from Fundus Image Using Scaled Grid

Abstract: This paper proposes an automated technique to segment the retinal blood vessels from funduscopic images An Adaptive Line Structuring Element (ALSE) [12] is used for initial segmentation, but the process introduces large number of noisy objects accompanying the vessel structure Fortunately, these noisy objects are relatively isolated structures in comparison to the blood vessels So, a suitably Scaled Grid can be used to delimit the noisy objects from its neighborhood When an object falls fully inside a block of the grid, it is considered as a noise and is eliminated But the objects which passes over the boundary of a block are preserved The scale of the grid is iteratively increased to identify eventually the all isolated objects and are eliminated without any loss of the actual vessel’s structure To measure the performance, Accuracy, Sensitivity and Specificity are calculated and compared with the recently found algorithms proposed in the literature

Automatic Brain Tumor Segmentation Using Multi-OTSU Thresholding and Morphological Reconstruction

Abstract: Images segmentation aims to divide an image into several segments. They can be selected according to the composition of the region of interest, the types of tissues, and the functional zones [1]. In this paper, we present a new segmentation method, based on the multi-thresholding method and morphological reconstruction for brain tumor separation from Magnetic Resonance Imaging (MRI). Firstly, we use a pre-processing to enhance image contrast and quality by intensity adjustment. Secondly, the improved image is segmented using the multi-Otsu method and finally, a morphological reconstruction was performed with the appropriate structuring element parameter on the segmented image to determine the tumor. A comparison with some state-of-the-art algorithms demonstrated the efficiency of the proposed method with regard to accuracy.

Abstract Mathematical morphology based on structuring element: Application to morpho-logic.

Abstract: A general definition of mathematical morphology has been defined within the algebraic framework of complete lattice theory. In this framework, dealing with deterministic and increasing operators, a dilation (respectively an erosion) is an operation which is distributive over supremum (respectively infimum). From this simple definition of dilation and erosion, we cannot say much about the properties of them. However, when they form an adjunction, many important properties can be derived such as monotonicity, idempotence, and extensivity or anti-extensivity of their composition, preservation of infimum and supremum, etc. Mathematical morphology has been first developed in the setting of sets, and then extended to other algebraic structures such as graphs, hypergraphs or simplicial complexes. For all these algebraic structures, erosion and dilation are usually based on structuring elements. The goal is then to match these structuring elements on given objects either to dilate or erode them. One of the advantages of defining erosion and dilation based on structuring elements is that these operations are adjoint. Based on this observation, this paper proposes to define, at the abstract level of category theory, erosion and dilation based on structuring elements. We then define the notion of morpho-category on which erosion and dilation are defined. We then show that topos and more precisely topos of presheaves are good candidates to generate morpho-categories. However, topos do not allow taking into account the notion of inclusion between substructures but rather are defined by monics up to domain isomorphism. Therefore we define the notion of morpholizable category which allows generating morpho-categories where substructures are defined along inclusion morphisms. {A direct application of this framework is to generalize modal morpho-logic to other algebraic structures than simple sets.

Morphological contour decomposition and reconstruction by using asymmetric SEs

Abstract: We had proposed an algorithm based on mathematical morphology to decompose and reconstruct an image by using two contours [4]. These contours are defined as the edge and the second edge (i.e., the edge of the image which has been removed an edge once) of the original image. Since these contours have informations of boundary and internal direction of image, the original image can be reconstructed exactly from them. However, there was a restriction in this algorithm that the structuring element (SE) used for making contours must be symmetric. In this paper, we will show an expansion of the algorithm to be able to use asymmetric SEs under a certain condition and also show some applications of this method.

Morphological filtering of free-form surfaces

Abstract: Morphological filters are E-system-type filters, which are complementary to M-system filters that are based on mathematical morphology. They are the superset of the early envelope filters but offer more tools and capabilities. This chapter first reviews the historical development of morphological filters along with the associated basic theories of morphological operations. The applications of morphological operation in the field of geometrical metrology are illustrated. The chapter then presents two advanced computational algorithms that allow morphological filters to be applied to free-form surfaces. The first algorithm is based on a computational geometry technique, that is, alpha shape theory, which can be further optimized by searching contact points and using the divide and conquer technique. The second algorithm removes the dependence of the alpha shape method on the Delaunay triangulation and recursively searches the contact points of the structuring element and the surface. In conclusion, this chapter will present and evaluate examples of applying morphological filters on precision-machined surfaces and additive manufactured surfaces.