Showing papers on "Structuring element published in 2014"

Extraction of bridges over water from high-resolution optical remote-sensing images based on mathematical morphology

Abstract: Bridges over water are typical man-made structures on the land’s surface. An accurate extraction of such bridges from high-resolution optical remote-sensing images plays an important role in civil, commercial, and military applications. Considering the complex features of ground objects within high-resolution optical remote-sensing images and the inefficiency of previous methods of bridge extraction with random bridge orientation, direction-augmented linear structuring elements were constructed and applied in this study by using mathematical morphology to identify and extract bridges over water with different orientations. First, the image pre-processing is performed to facilitate the object extraction. Then by using the histogram-based threshold segmentation method, water bodies such as rivers are extracted and described as a binary image. Based on water bodies, the appropriate direction-augmented linear structuring element is then selected. Together with mathematical morphology operations, such as dilat...

Adaptive Mathematical Morphology for QRS fiducial points detection in the ECG

Abstract: Fixed structure Mathematical Morphology (MM) operators have been used to detect QRS complexes in the ECG. These schemes are limited by the arbitrary setting of threshold values. Our study aims at extracting QRS complex fiducial points using MM with an adaptive structuring element, on a beat-to-beat basis. The structuring element is updated based on the characteristics of the previously detected QRS complexes. The MIT-BIH arrhythmia and Physionet QT databases were respectively used for assessing the performance of R-waves and other fiducial points detection. Results show comparable or better performance than the state-of-the-art and an efficient extraction of Q- and S-waves as well as onset and offset points of the QRS complex.

Melanoma detection using fuzzy C-means clustering coupled with mathematical morphology

Abstract: This paper proposes a Fuzzy C-Means (FCM) based approach designed for melanoma diagnosis. The methodology comprises the traditional data processing architecture, including pre-processing (contrast stretching), main processing (FCM) and post-processing (morphological erosion). The contrast stretching phase has the purpose of stretching the range of pixel intensities of the input image to occupy a larger dynamic range in the output image. This is followed by the FCM algorithm, which automatically divides the data provided by the contrast stretching phase into two clusters: lesion and skin. This process ends with the morphological erosion of the segmented image, where the structuring element is translated over each pixel of the object, so as to overcome typical irregularities between lesion and skin (e.g., irregular boundaries, dark hair covering the lesions, specular reflections, among others). The proposed approach is evaluated in dermatoscopic images of skin cancer, and results show that it is able to produce accurate identification of lesions.

Filtering of mixed Gaussian and impulsive noise using morphological contrast detectors

Abstract: Several morphological transformations to detect noise are introduced. The initial method is a modification of a procedure presented previously in the current literature. The proposals given in the study allow to detect noise in two ways: (i) using a contrast measure and (ii) applying different proximity criteria into several proposed toggle mappings. In the end, two of the proposals given in this study yield a better performance with respect to methods in which this research is based. However, although the methodology to identify noise works adequately, the results are limited due to the use of the structuring element. In Section 4, an image with two types of noise is cleaned. Such image is contaminated with zero mean Gaussian noise with 0.01 variance and 5% of salt and pepper noise. From this experiment, the proposal giving the best performance is selected; subsequently, this is compared with other recent operators as PDEs, wavelets, morphological connected rank max opening and amoebas.

A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis

Abstract: Mathematical morphology (MM) is an efficient nonlinear signal processing tool It can be adopted to extract fault information from bearing signal according to a structuring element (SE) Since the bearing signal features differ for every unique cause of failure, the SEs should be well tailored to extract the fault feature from a particular signal In the following, a signal based triangular SE according to the statistics of the magnitude of a vibration signal is proposed, together with associated methodology, which processes the bearing signal by MM analysis based on proposed SE to get the morphology spectrum of a signal A correlation analysis on morphology spectrum is then employed to obtain the final classification of bearing faults The classification performance of the proposed method is evaluated by a set of bearing vibration signals with inner race, ball, and outer race faults, respectively Results show that all faults can be detected clearly and correctly Compared with a commonly used flat SE, the correlation analysis on morphology spectrum with proposed SE gives better performance at fault diagnosis of bearing, especially the identification of the location of outer race fault and the level of fault severity

Horizontal and vertical line detection and removal for document images

Abstract: A vertical and horizontal line detection method for document images includes generating multiple binary images from the input grayscale document image based on multiple binarization thresholds, detecting horizontal and vertical lines in each of the multiple binary images independently, and merging the detection results from the multiple binary images. The line detection process for each binary image include applying an opening operation using a vertical or horizontal line as the structuring element, and removing connected components that are not vertical or horizontal lines based on a stroke width analysis. The boundaries of the detected lines are obtained using horizontal and vertical projections.

Fast algorithm for dilation and erosion using arbitrary flat structuring element: Improvement of urbach and Wilkinson's algorithm to GPU computing

Abstract: Mathematical morphology is widely used in image processing. Since all morphological operations are based on evaluating values contained in a local neighborhood, calculations on large neighborhood are particularly computationally intensive. Fast implementations for morphological operations do exist but are mostly only adapted to structuring elements of a limited size and/or a specific shape. Therefore, difficulties appear when an arbitrary or complex shape like a circle of a large size is needed. Since dilation and erosion can be used for the representation of all morphological operations, an efficient implementation of those operators is fundamental. Thus, this paper presents a new efficient algorithm for these basic operators of mathematical morphology. It enables the use of any shape of flat structuring element for grayscale images up to 3 dimensions. Also, we have defined this method as an iterative algorithm so it can be used on any parallel architecture and in particular, for GPU (Graphic Processor Unit) computing.

Morphological Amoebas and Partial Differential Equations

Abstract: Morphological amoebas are image-adaptive structuring elements introduced by Lerallut, Decenciere, & Meyer Their construction relies on a distance measure that combines spatial distance with gray-value contrast (tonal distance) Amoebas can be used with various morphological filters In connection with median filtering, they lead to an image enhancement filter with segmentation-like properties In this chapter, we consider different amoeba-based iterative image filters and study their relations to partial differential equations (PDEs) In a continuous formulation, the iterated amoeba median filter asymptotically approximates the well-known self-snakes partial differential equation (PDE) Different edge-stopping functions in the PDE can be related to different metrics used in amoeba construction PDE approximation results for further amoeba-based filters, as well as for an amoeba-based active contour segmentation method, are presented Furthermore, we address the role of presmoothing in the self-snakes equation, and relate it to the nonzero structuring element radius in computations with amoeba models Experiments demonstrate the validity of central theoretical results

New morphological methods to generate two-dimensional curve offsets

Abstract: For the purpose of generating 2D curve offsets used in 2.5D machining, four new methods based on morphological operations on different mathematical entities are presented in this paper. All of the methods, which lend themselves for parallel processing, exploit the idea that the boundaries formed by a circular structuring element whose center sweeps across the points on a generator/base curve comprise the entire offsets of the progenitor. The first approach, which is a carry-over from image processing, makes good use of morphological operations on binary images to produce 2D offsets via contour tracing algorithms. The second method, which is to rectify the high memory cost associated with the former technique, utilizes morphological operations on (boundary data) sets. The implementation of this basic technique is illustrated by two Matlab functions given in the paper. Despite its simplicity, the time complexity of this paradigm is found to be high. Consequently, the third method, which is evolved from the preceding one, reduces the time complexity significantly with the utilization of a geometric range search method. This technique, which has a considerable margin for improvement, is found to be suitable to be used as a part of the real-time motion command generator for CNC applications. Unlike the previous schemes, the final approach uses polygon operations to generate such curves. The run-time of this technique is highly governed by the complexity of the polygon overlay algorithm selected. The paper analyzes the complexity of each technique. Finally, the presented methods are evaluated (in terms of run-time and geometric accuracy) via two test cases where most CAD/CAM packages fail to yield acceptable results.

Optimization Design of Mathematical Morphology Filter Based on Quantum Genetic Algorithm

Abstract: To deal with the structuring element optimization of mathematical morphology filter,an optimization design method of mathematical morphology filter based on quantum genetic algorithm was proposed. The quantum genetic population was initialized according to the characteristic of mathematical morphology structuring element parameters. The population evolution was realized by quantum crossover,variation and quantum rotation gate based on expansion coefficient,thus obtaining the best parameters of the mathematical morphology filter. The performances of optimization algorithm under different proportions of random noise and power frequency interference noise were studied. The simulation results show that the performance of mathematical morphology filter is greatly improved after being optimized. The signal-to-noise ratio of the signal with random noise is improved from- 0. 98 dB to 5. 23 dB,and that of the signal with mixed noise is improved from- 3. 05 dB to 0. 41 dB. It means that the optimized mathematical morphology filter could remove both random noise and mixed noise with power frequency interference effectively.

Brain Tumor Segmentation Based on Structuring Element Map Modification and Marker-controlled Watershed Transform

Abstract: Brain medical images are generally prone to noise and also fraught with intensity heterogeneity within the tumor. Fuzzy and boundary discontinuity caused by the tumor also adversely affects the accuracy of the tumor segmentation. A method based on morphological structuring element map modification and marker-controlled watershed segmentation is proposed. Firstly, a structuring element map is constructed according to the sum of the weighted variance of the specific regions within morphological gradient image, and each value of the structuring element map represents the size of structuring element (SE). Secondly, the original image is modified by morphological opening-closing, where the size of SE are determined by the structuring element map in the corresponding pixel, such an adaptive image modification can eliminate the noise and small regular details while preserve the larger object contours without less location offsets. Finally, marker-controlled watershed transform is used to complete the tumor segmentation. Experiments show that the method ensures brain tumors are more accurately segmented.

Морфологические дескрипторы формы бинарных изображений на основе эллиптических структурирующих элементов

Abstract: In this paper, we proposed algorithms for constructing continuous skeletons with elliptical structuring element (SE). The transformation between disk and elliptical skeletons is described. Computationally efficient discrete-continuous approach for the construction of morphological descriptors (spectra and maps) with fixed elliptical SE is proposed based on elliptical skeletons. The definitions of morphological elliptical maps, size spectra, directions spectra, elongation spectra with arbitrary elliptical structuring element is proposed. Definitions of two-dimensional spectra with various combinations of size and shape factors is given. Proposed morphological descriptors can be used directly for shape comparison or for shape segmentation into simple geometric parts of specified thickness, direction and elongation.

Watershed segmentation based on gradient relief modification using variant structuring element

Abstract: Watershed segmentation is suitable for producing closed region contour and providing an accurate localization of object boundary. However, it is usually prone to over-segmentation due to the noise and irregular details within the image. For the purpose of reducing over-segmentation while preserving the location of object contours, the watershed segmentation based on morphological gradient relief modification using variant structuring element (SE) is proposed. Firstly, morphological gradient relief is decomposed into multi-level according to the gradient values. Secondly, morphological closing action using variant SE is employed to each level image, where the low gradient level sets use the large SE, while the high gradient level sets use the small one. Finally, the modified gradient image is recomposed by the superposition of the closed level sets, and watershed transform to the modified gradient image is done to implement the final segmentation. Experimental results show that this method can effectively reduce the over-segmentation and preserve the location of the object contours.

Edge Detection using Morphological Operator: A New Approach

Abstract: Edges are regions of interest where there is a sudden change in intensity. These features play an important role in object identification methods commonly used for many applications in computer vision and pattern recognition. This paper presents methods for edge detection using morphological operator. Morphological edge detector heavily relies on the choice of the structuring element (SE) and the results will vary from one SE to other. Therefore, the novel approach is used to find the SE from the image itself using freeman chain code, which is then followed by the Morphological Gradient method to detect edges. The proposed method is very simple, efficient and fast. An experimental result on various images shows all the prominent edges efficiently.

Fast morphology algorithm with parallel processing structures

Abstract: Morphology, including erosion and dilation, is important for many image processing applications, such as shape analysis, pattern recognition, denoising, and segmentation. In this paper, an efficient way to perform erosion and dilation operations is proposed. Since the proposed algorithm deals with each row and column separately, it can be implemented in a parallel processing structure, which can significantly reduce the computation time. Moreover, recursive structures can also be adopted in the proposed algorithm. In addition to less computation time, the memory requirement of the proposed algorithm is also very less. The proposed algorithm is suitable for any structuring element of the symmetric and convex form. When using different structuring element, only a very small part of the structure should be adjusted. The proposed algorithm can much improve the efficiency of morphology and is suitable for both software and hardware implementation.

Spatially-Variant Area Openings for Reference-Driven Adaptive Contour Preserving Filtering

Abstract: Classical adaptive mathematical morphology is based on operators which locally adapt the structuring elements to the image properties. Connected morphological operators act on the level of the flat zones of an image, such that only flat zones are filtered out, and hence the object edges are preserved. Area opening (resp. area closing) is one of the most useful connected operators, which filters out the bright (resp. dark) regions. It intrinsically involves the adaptation of the shape of the structuring element parameterized by its area. In this paper, we introduce the notion of reference-driven adaptive area opening according to two spatially-variant paradigms. First, the parameter of area is locally adapted by the reference image. This approach is applied to processing intensity depth images where the depth image is used to adapt the scale-size processing. Second, a self-dual area opening, where the reference image determines if the area filter is an opening or a closing with respect to the relationship between the image and the reference. Its natural application domain are the video sequences.

Real-Time Edge Detection Using Dynamic Structuring Element

Abstract: The morphological gradient is the classical, useful tool in image edge enhancement, but it can be insufficient in real-time applications due its complexity, as are different modifications of this method proposed in literature to obtain clearer edge information. In this paper a novel, simple and fast morphological edge detector is presented. It is based on the definition of a non-flat, dynamic structuring element and on the new definition of a simple morphology operation, useful as a generalisation of many image processing operations. The several results of the algorithm’s application are presented, as along with the results of its implementation in a real-time IR and TV image fusion hardware system.

Application of multiple order morphology transformation on edge detection for liver CT image

Abstract: Edge detection of CT image can help doctors to diagnose and tracing observe more accurately, order morphology is an expansion of basic mathematical morphology, order morphology transformation is the principal conception of order morphology. The paper researches the application of edge detection for liver CT image using multiple order morphology transformation: analyzes the influence on edge detection of different structuring elements and different percentiles. Experimental results showed order morphology transformation has both the pertinence and the flexibility for edge detection of CT image.

Texture image segmentation based on the elements of Gray Level Aura Matrices

Abstract: We present a method for texture image segmentation based on Gray Level Aura Matrices (GLAMs). The GLAMs allow describing the relationship between the target pixel and its neighboring pixels located in neighborhood structure defined by a structuring element. Their elements are directly used in this paper instead of Haralick features in order to characterize each pixel of the image. The pixels having the same features are then gathered into classes using the Fuzzy C-means algorithm. Experiments results on synthetic and real images show the relevance of the elements of GLAMs in the segmentation of images with different textures.

A Thresholding Approach for Text Extraction in Handwritten Historical Documents Using Adaptive Morphology

Abstract: The aim of preserving historical handwritten documents is to restore the degraded text containing information. But generally global threshold fails to restore the text adequately. Adaptive (local) thresholding is required for preserving the text in these documents. In recent past many standard adaptive thresholding methods have been proposed for binarization of handwritten text document images. We propose a new adaptive thresholding method using locally adaptive mathematical morphology. Formulation of an adaptive structural element is a challenging work and addressed recently by some researchers. Our method at initial step binarizes the image applying global threshold. The residual background image below threshold containing low intensity texts mixed with noise is further processed. A new approach for constructing spatially variant operator corresponding to local variances is proposed. Gaussian surface is selected as an adaptive gray-scale structuring element for mathematical morphological operations (opening and closing), whose parameters base and height depends on local variance. The proposed method successfully demises various kinds of degraded documents enhancing textures with clear background. Experimental result on real historical handwritten document and artificial images show that our method outperforms several other existing methods both visually and using some evaluation metrics.

Implementing Mathematical Morphology Techniques to Detect Cracks in Underground Pipeline Images Automatically

Abstract: An oil pipe buried under needs to be checked for their current quality before oil transportation takes place through it. The method involved for all these days were manual using “PIGS”. The manual work done by a human operator was hectic, to overcome this situation, a computerized automated image processing technique is introduced through this paper, where the image analysis or pattern analysis is evaluated using the Mathematical Morphology. Mathematical Morphology accomplishes to detect the cracks using Set Theory and also Curvature evaluation for segment images with respect to a precise geometric model to define crack like patterns. This paper describes the method, background of the theory discussed and evaluation of the theory used to identify the defects. Based on the Mathematical Theories of sets and topological notations, its principle lies in studying the Morphological properties (Shape, Size, Orientation and other forms) of the object(Patterns) through non-linear transformations associated with a reference object(SE-Structuring Element).At the end of this paper, image processing to detect the cracks is achieved.

Mathematical morphology pre-processing for enhanced segmentation of heterogeneous spatial regions

Abstract: The very high spatial resolution of Pleiades images allows for the detection of small spatial objects such as buildings or isolated trees. However, the delineation of spatial regions, defined as associations between different spatial objects (such as open canopy forests or urban areas), becomes more challenging with the high level of details. On one hand, automated image segmentation algorithms often yield over-segmented polygons due to due to the high spectral heterogeneity of those regions. On the other hand, manual delineation was shown to end up with a significant bias from the interpreter and even a lack of consistency when the same person works more than one hour on the same task. In this study, we aimed at implementing a new filter to increase the contextual consistency of automated segmentation while preserving the geometric precision of the delineation of spectrally homogeneous spatial regions. A new mathematical morphology approach is proposed, which consists in applying a set of rules to an image based on the presence of absence of vegetation pixels within a structuring element. Two composite filters were then built based on the new filters. The opening filter removes isolated vegetation patches inside heterogeneous spatial regions, while the closing filter fills the gaps between those vegetation patches. The filters have been tested on a Pleiades images located in Belgium around the city of Leuven. A composite image was then created with the NIR and Red filtered bands stacked with the original image bands. The composite and the original bands were then segmented using e-Cognition software with the same parameters. The results show that the segmentation of the filtered images is spatially more consistent than the segmentation based on the unfiltered image. The over-segmentation is reduced in the heterogeneous areas, while the precision of the delineation is improved. The objects derived from the filtered images are thus more appropriate for the monitoring of spatial regions.

Extraction of Fine Blood Vessels from an Ultrasound Image by an Image Processing

Abstract: This paper discusses some useful image processing techniques for an ultrasound image using adaptive morphological operations. They are defined in such a way that the used structuring element is a function of local image to respond its characteristics. The defined mathematical morphology has almost the same mathematical structure and properties as the conventional one. Besides, the former has an advantageous image processing to others. For example, this technique to ultrasound images to extract blood vessel and showed its useful function. We discussed parameter evaluation and setting, from a statistical point of view. Experimental results are also demonstrated.

Mathematical Morphology based Image Enhancement and Background Detection

Abstract: This paper deals with analysis of image Intensification carried out various methodologies used in Mathematical Morphological (MM) theory on poor lighting images. In this paper, Some Morphological Transformation have been processed through Block Analysis, Morphological Operation and Opening by Reconstruction on dark Images. Basically, Image enhancement and Background detection is illustrated through Weber's Law Operator. Weber Law consists of two operators as block analysis while second operator utilize opening by reconstruction to define multi background notion. Such Morphological operations are Erosion, Dilation, Composite operation such as Opening and Closing. In Mathematical Morphology there is transformation that allows filtering of the Image with new contour leads to Opening by reconstruction and closing by reconstruction as well. Analysis of above mention methods illustrated through the processing of images with filtering techniques along with different dark background images.

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.

Color Image Indexing Using Mathematical Morphology

Abstract: A mathematical morphology based approach for color image indexing is explored in this paper. Morphological signatures are powerful descriptions of the image content in the framework of mathematical morphology. A morphological signature (either a pattern spectrum or a differential morphological profile) is defined as a series of morphological operations (namely openings and closings) considering a predefined pattern called structuring element. For image indexing it is considered a morphological feature extraction algorithm which includes more complex morphological operators: i.e. color gradient, homotopic skeleton, Hit-or-Miss transform. In the end, illustrative application examples of the presented approach on real acquired images are also provided.

Trajectory-Based Morphological Operators: A Model for Efficient Image Processing

Abstract: Mathematical morphology has been an area of intensive research over the last few years. Although many remarkable advances have been achieved throughout these years, there is still a great interest in accelerating morphological operations in order for them to be implemented in real-time systems. In this work, we present a new model for computing mathematical morphology operations, the so-called morphological trajectory model (MTM), in which a morphological filter will be divided into a sequence of basic operations. Then, a trajectory-based morphological operation (such as dilation, and erosion) is defined as the set of points resulting from the ordered application of the instant basic operations. The MTM approach allows working with different structuring elements, such as disks, and from the experiments, it can be extracted that our method is independent of the structuring element size and can be easily applied to industrial systems and high-resolution images.

Automated Surface Profile Measurement of Printed Circuit Boards by Phase-Shifted Shadow Moiré

Abstract: Phass-shifted shadow Moire has gained more applications in electronic industry. However, printed circuit boards (PCB) may contain many cavities or specular materials on the surface that make the phase-unwrapping of Moire fringe patterns more difficult or fail. In this paper, a method is proposed to overcome this difficulty to process the phase-shifted fringe patterns effectively and automatically for surface profile measurement. Firstly the intensity values of the original four phase-shifted fringe patterns are averaged and differentiated to enhance the erroneous spots. Then the median grey-level value of the enhanced image is used as the threshold to binarize the enhanced image to find the erroneous bright and black spots. According to the largest size of erroneous spot, the size of a structuring element is determined for morphology filtering. Thereafter the phase can be calculated and unwrapped correctly. Test of the method on a PCB is demonstrated and discussed.