Morphological Structuring Elements Research Articles

Short Words for Writer Identification Using Neural Networks

In biometrics, it is desirable to distinguish a person using only a short sample of his handwriting. This problem is treated in the present work using only a short word with three letters. It is shown that short words can contribute to high-performance writer identification if line characteristics are extracted using morphological directional transformations. Thus, directional morphological structuring elements are used as a tool for extracting this kind of information with the morphological opening operation. The line characteristics are organized based on Markov chains so that the elements of the transition matrix are used as feature vectors for identification. The Markov chains describe the alternation in the directional line features along the word. The analysis of the feature space is carried out using the Fisher linear discriminant method. The identification performance is assessed using neural networks, where the simplest neural structures are sought. The capabilities of these simple neural structures are investigated theoretically concerning the achieved separability into the feature space. The identification capabilities of the neural networks are further assessed using the leave-one-out method. It is proved that the neural methods achieve identification performance that approaches 100%. The significance of the proposed method is that it is the only one in the literature that presents high identification performance using only one short word. Furthermore, the features used as well as the classifiers are simple and robust. The method is independent of the language used regardless of the direction of writing. The NIST database is used for extracting short-length words having only three letters each.

Classification for transient overvoltages in offshore wind farms based on multi-scale mathematical morphology

At present, the transient overvoltages in o shore wind farms caused by faults or frequent operations of electrical equipment are particularly severe. The quick and accurate classi cation of transient overvoltages is important for protecting electrical equipment from damage. In order to classify the internal transient overvoltages in o shore wind farms, this research rstly proposes a feature extraction method based on mathematical morphology, which proposes a new morphological structure element and utilizes a multi-scale mathematical morphology to extract the high/low-frequency components of transient overvoltages. Then a high-frequency feature and a high/low-frequency energy ratio feature are constructed as identi cation features. Finally, based on the constructed features, a support vector machine is employed to identify di erent types of internal transient overvoltages. Extensive simulations and experiments are performed to verify the superiority of the proposed feature extraction method based on mathematical morphology, which is also compared with the widely used conventional wavelet algorithms. Results show the proposed mathematical morphology feature extraction method is capable of classifying and discriminating among various types of internal transient overvoltages with a simple procedure and an improved accuracy. The proposed method provides a valuable reference for the protection and insulation coordination of electrical equipment in o shore wind farm substations.

Nuclide Spectrum Peak Searching Algorithm Based on Multiple Morphological Structuring Elements

Considering the characteristics of the traditional peak searching methods of nuclide spectrum and morphological transformation in the image processing, a nuclide spectrum peak searching method based on multiple morphological structuring elements is proposed. Four flat structural elements at an angle of 0, 45, 90, and 135 to the horizontal line are utilized, then the morphological transformation of the nuclide spectrum is performed, and the details of the different directions of the nuclide spectrum curve are better controlled. The algorithm proposed in this paper improves the ability of the morphological peak searching and the peak information is preserved well, meanwhile, the method has a better peak recognition effect.

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

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.

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

ABSTRACTChannels 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 in deep-water sedimentary basins. However, manual interpretation of channels is time-consuming and needs skill and experience. Therefore, in this paper, using morphological filters, we present an algorithm for automatically extracting the channel edges from seismic data. We used a morphological gradient algorithm with an ellipsoidal adaptive structuring element to detect edges. Morphological gradient operators highlight the image edges within the neighbourhood of the structuring element. The adaptive structuring element at each point of the data align with the edges and contours at that point, and so can be more effective in edge detection. Eigenvalue decomposition of the gradient structure tensor gives an estimate of the orientation and anisotropy rate of objects within the data. Hence, parameters of the adaptive structuring element are obtained from the eigenvalues of a 3D gradient structure tensor. Finally, the proposed algorithm is applied to synthetic and real seismic data containing a channel, and the results are compared with other well-known edge detection methods such as Canny, Sobel, semblance and negative curvature. The proposed algorithm was able to correctly identify edge locations, and also outperforms other methods in the presence of noise.

Single-Camera Computer Vision Algorithm for Robot Shortest Path Estimator using morphological structuring element with variable sizes

The robotic challeng of developing and implementing a shortest-path finding algorithm to reach a destination without bumping into obstacles has been tackled in this paper. The proposed algorithm is utilized a single ceiling fixed camera and based on the mask and robot size with different sizes for dilation mask. The implementation has been successfully produced thirty-three computer vision figures and two comparison tables of simulation results by using morphological structuring element with different sizes. This paper’s proposal of optimal path finding algorithm is effeciently save robot energy in reaching targets.

Estimating forest parameters using Landsat ETM+ spectral responses and monocultured plantation fieldwork measurements data

ABSTRACTForest parameters, such as mean diameter at breast height (DBH), mean stand height (H) or volume per hectare (V), are imperative for forest resources assessment. Traditional forest inventory that is usually based on fieldwork is often difficult, time-consuming, and expensive to conduct over large areas. Therefore, estimating forest parameters in large areas using a traditional inventory approach combined with satellite data analysis can improve the spatial estimates of forest inventory data, and hence be useful for sustainable forest management and natural resources assessment. However, extracting practical information from satellite imagery for such purpose is a challenging task mainly because of insufficient knowledge linking forest inventory data to satellite spectral response. Here, we present the use of a cost-free Landsat-7 Enhanced Thematic Mapper Plus (ETM+) in order to explore whether it is possible to combine all available optical bands from a specific sensor for improving forest parameter spatial estimates, based on fieldwork at Lahav and Kramim Forests, in the Israeli Northern Negev. A generic strategy, based on morphological structuring element, convex hall and spectral band linear combination algorithms, was developed in order to extract the mathematical dependencies between the forest inventory measurements and linear combination sets of Landsat-7 ETM+ spectral bands, which yields the highest possible correlation with the forest inventory measured data. Using the mathematical dependency functions, we then convert the entire Landsat-7 ETM+ scenes into forest inventory parameter values with sufficient accuracy and tolerance errors needed for sustainable forest management. The root mean square error obtained between the measured and the estimated values for Lahav Forest are 0.70 cm, 0.29 m, and 1.48 m3 ha−1 for the mean DBH, H, and V, respectively, and for Kramim forest are 0.61 cm, 0.70 m, and 6.31 m3 ha−1, respectively. Furthermore, the suggested strategy could also be applied with other satellites data sources.

Polyenergetic Known-Component Reconstruction without Prior Shape Models.

Previous work has demonstrated that structural models of surgical tools and implants can be integrated into model-based CT reconstruction to greatly reduce metal artifacts and improve image quality. This work extends a polyenergetic formulation of known-component reconstruction (Poly-KCR) by removing the requirement that a physical model (e.g. CAD drawing) be known a priori, permitting much more widespread application. We adopt a single-threshold segmentation technique with the help of morphological structuring elements to build a shape model of metal components in a patient scan based on initial filtered-backprojection (FBP) reconstruction. This shape model is used as an input to Poly-KCR, a formulation of known-component reconstruction that does not require a prior knowledge of beam quality or component material composition. An investigation of performance as a function of segmentation thresholds is performed in simulation studies, and qualitative comparisons to Poly-KCR with an a priori shape model are made using physical CBCT data of an implanted cadaver and in patient data from a prototype extremities scanner. We find that model-free Poly-KCR (MF-Poly-KCR) provides much better image quality compared to conventional reconstruction techniques (e.g. FBP). Moreover, the performance closely approximates that of Poly- KCR with an a prior shape model. In simulation studies, we find that imaging performance generally follows segmentation accuracy with slight under- or over-estimation based on the shape of the implant. In both simulation and physical data studies we find that the proposed approach can remove most of the blooming and streak artifacts around the component permitting visualization of the surrounding soft-tissues. This work shows that it is possible to perform known-component reconstruction without prior knowledge of the known component. In conjunction with the Poly-KCR technique that does not require knowledge of beam quality or material composition, very little needs to be known about the metal implant and system beforehand. These generalizations will allow more widespread application of KCR techniques in real patient studies where the information of surgical tools and implants is limited or not available.

Removal of the heavy metal ions from aqueous solution using modified natural biomaterial membrane based on silk fibroin

Water-insoluble silk fibroin (WSF) membrane was prepared by co-blending and interaction of fibroin and silane coupling agent. A novel Schiff base with a chelating group, 3,5-bis-{2-hydroxyphenyl-5- [(2-sulphate-4- sulphatoethylsulphonyl-azobenzol) methylene amino]} benzoic acid (BHSABA), was applied to modify WSF membrane. The morphological structure and surface element analysis of WSF and its modified membrane (MWSF) were investigated by scanning electron microscopy (SEM) and energy dispersive spectrometer (SEM-EDS). The sorption behavior of MWSF membrane for six metal ions, Cu(II), Co(II), Ni(II), Cr(III), Pb(II) and Cd(II), were measured. MWSF membrane had the significantly selective sorption for Pb(II) and Cd(II) metal ions and the removal percentage being 82% and 56%, respectively. The adsorption kinetics of MWSF membrane for Cd(II) was analyzed by the pseudo-first-order and pseudo-second-order equations. The adsorption removal process for Cd(II) metal ion was in line with the pseudo-second-order model. MWSF membrane as bioabsorbent has potential application for removal heavy metal ions from the aqueous solution.

Comparative Study of Lung ROI Segmentation with Different Morphological Structuring Element Sizes in Noise Environment

Comparative Study of Lung ROI Segmentation with Different Morphological Structuring Element Sizes in Noise Environment

A Novel Vehicle Detection Method With High Resolution Highway Aerial Image

A robust and efficient vehicle detection method from high resolution aerial image is still challenging. In this paper, a novel and robust method for automatic vehicle detection using aerial images over highway was presented. In the method, a GIS road vector map was used to constrain the vehicle detection system to the highway networks. After the morphological structure element was identified, we utilized the grayscale opening transformation and grayscale top-hat transformation to identify hypothesis vehicles in the light or white background, and used the grayscale closing transformation and grayscale bot-hat transformation to identify the hypothesis vehicles in the black or dark background. Then, targets with large size or covering a large area were sieved from the hypothesis vehicles using an area threshold that is much larger than a typical vehicle. Targets, whose width is narrower than the diameter of structure element utilized in the grayscale morphological transformation, were smoothed out from the hypothesis vehicles using binary morphological opening transformation. Finally, the hypothesis vehicles detected in both cases were overlaid. It should be noted that in the detection system, a vehicle could be detected twice by the two approaches. The two identical hypothesis vehicles should be amalgamated into a single one for accuracy assessment subsequently. We tested our system on seventeen highway scenes of aerial images with a spatial resolution of 0.15 × 0.15 m. The experimental results showed that the correctness, completeness, and quality rates of the proposed vehicle detection method were about 98%, 93%, and 92%, respectively. Thus, our proposed approach is robust and efficient to detect vehicles of highway using high resolution aerial images.

Algorithm for decomposition of binary morphological convex structuring elements based on periodic lines

This paper presents a new and simple algorithm for decomposing a convex structuring element into dilations of periodic lines with a union of a residue component. The algorithm proposed in this paper is based on the boundaries of convex structuring elements and the properties of dilations. Besides simplicity of the algorithm, the advantage of the method is that periodic lines possess a faster implementation of the corresponding dilations and erosions. The examples of optimal decomposition demonstrate that the algorithm offers a low complexity of morphological operators. The results indicate that the present decomposition algorithm is more robust when compared with other algorithms.

Improved Pavement Distress Detection Based on Contourlet Transform and Multi-Direction Morphological Structuring Elements

Traditional methods for crack distress detection cannot capture the geometric information of images and tend to amplify noise. In order to solve this problem, an improved algorithm based on contourlet transform and multi-direction morphological structuring elements is proposed. The new algorithm decomposed image into approximation coefficients and detail coefficients. Morphological erode operations is used to distinguish noise form detail information according to dependencies of contourlet coefficients, then nonlinear mapping function is used to modify the contourlet coefficients. And the enhanced image is obtained by contourlet inverse transform. Compared with other traditional methods, the experimental results indicate that our method can effectively extract the edges of cracks and evidently decrease the influence of noises. Moreover, it can provide good image processing speed.

Dual Primitive Estimation of Textures

A method of estimating dual primitives in a textural image is proposed. This method is based on the Primitive, Grain, and Point Configuration (PGPC) texture model, which regards a texture as an arrangement of grains derived from one or a few primitives. Appropriate primitives can be represented by morphological structuring elements estimated from a texture. Conventional primitive estimation methods estimate only one primitive from each textural image. However, they do not work well on textural images that contain more than one basic structure, since two or more types of grain cannot be generated from only one primitive. The proposed method simultaneously estimates two optimal structuring elements of a texture. The experimental results show that the proposed method provides more representative estimations than the conventional method.

Trace elements content and hormonal profiles in women with androgenetic alopecia

It is well-known that some trace element imbalances play a significant role in the pathomechanism of many forms of alopecia. Androgenetic alopecia, however, is a specific local sensitivity of hair follicle receptors to androgens. In a clinical and laboratory study, 153 women with androgenetic alopecia (AGA) and 32 control women were examined. In AGA patients telogen hair and vellus hair (miniaturization, D < 30 μm) significantly differed in frontal and parietal hair comparison with occipital area (20 ± 0.9% vs. 12 ± 0.5% and 33 ± 0.9% vs. 12 ± 0.6% respectively). In the AGA group levels of androstenedione and dihydrotestosterone were higher than in the control group. Hair elemental content, analyzed by ICP-MS, demonstrated a lowered Cu and Zn content in the frontal area in comparison to the occipital area. It is important to note, that the AGA patients with elevated levels of androstenedione and dihydrotestosterone presented an increased Cu content and decreased Mn, Se, Zn contents in the occipital area of scalp. The occipital level of Cu positively correlated with the concentration of free testosterone in the serum. A negative correlation between the Zn content in the occipital area and the dehydroepiandrosterone level in the blood was found. Unfortunately, a routine treatment course of AGA patients, including topical inhibitor of 5-alpha-reductase and minoxidil, had no effect on the Cu hair content in occipital and frontal areas. However, there were positive changes in the morphological structure and other trace element contents. These data led us to hypothesize a key role of Cu metabolism disturbances in the AGA onset, development of AGA, and potential pharmaceutical targets for the treatment of AGA.

Clustering-driven watershed adaptive segmentation of bubble image

In order to extract froth morphological feature, a bubble image adaptive segmentation method was proposed. Considering the image’s low contrast and weak froth edges, froth image was coarsely segmented by using fuzzy c means (FCM) algorithm. Through the attributes of size and shape pattern spectrum, the optimal morphological structuring element was determined. According to the optimal parameters, some image noises were removed with an improved area opening and closing by reconstruction operation, which consist of image regional markers, and the bubbles were finely separated from each other by watershed transform. The experimental results show that the structural element can be determined adaptively by shape and size pattern spectrum, and the froth image is segmented accurately. Compared with other froth image segmentation method, the proposed method achieves much high accuracy, based on which, the bubble size and shape features are extracted effectively.

Morphological multiscale decomposition of connected regions with emphasis on cell clusters

After binarization of cells in complex cytological and histological images the segmented structures can be rather far away from a final quantification of features of single cells since cells may overlap and cluster strongly. Separating optically, partially or totally fused entities like cells is a problem which frequently cannot be solved by a watershed segmentation or a basic morphological processing of images. However, considering different morphological scales after iterative erosion gives rise to dominant markers of singular objects. Performing a reconstruction by iterative dilation yields a scale-independent decomposition of multiple disjointed cell clumps of different sizes within an image. Accordingly we developed a technique that splits cell clumps into meaningful parts. Since this method is based on the analysis of the morphological-scale space, generated by iterative erosion, it is independent on the size of cell clusters. The detection of dominant points within the eroded scales are cell-specific markers. The converse integration of markers at different scales is obtained by a successive reconstruction based on constrained dilation of the original cell shape. The advantages of this approach are the independence of cell shapes which are clumped, the consideration of holes or background intensities within objects and the robustness with regard to convergence. An important benefit is the fitting of the operation time to the size of clusters by the size of the morphological structuring element. This means, that this approach requires only one parameter. Finally, a better match of the morphological scale space approach was found and compared with the ground truth as well as the results of the watershed technique. The primary object of this paper is to highlight the algorithm and its results by using different examples from benchmark databases, self generated images that exhibit different topological features and complex configurations of cells within histological images.

Decomposition of binary morphological structuring elements based on genetic algorithms

Most image processing architectures adapted to morphological operations use structuring elements of a limited size. Various algorithms have been developed for decomposing a large sized structuring element into dilations of small structuring components. However, these decompositions often come with certain restricted conditions. In this paper, we present an improved technique using genetic algorithms to decompose arbitrarily shaped binary structuring elements. The specific initial population, fitness functions, dynamic threshold adaptation, and the recursive size reduction strategy are our features to enhance the performance of decomposition. It can generate the solution in less computational costs, and is suited for parallel implementation.

Decomposition of morphological structuring elements with integer linear programming

A method is proposed for decomposing morphological structuring elements based on integer linear programming. A decomposition problem is formulated into a set of linear constraints, and an optimal decomposition is a solution to the constraints, obtained by the cutting-plane simplex algorithm. The method has several advantages. It provides a systematic way of decomposing arbitrarily shaped structuring elements. For convex images, factors can be of any size, not restricted to 3 × 3; the candidate set can be freely assigned by the user; and the optimality criteria can be flexible.

Robust Descriptors of Binary Shapes with Applications

The subject of this paper is to propose and test a set of numerical descriptors of 2D binary planar shapes. Given a shape, A, the transformations of A with a given mathematical morphological operation and different structuring elements are considered. The measures of this family of transformed sets provide a numerical description of the original set A. These descriptors are very robust against noise and maintain a reasonable discriminatory power. The robustness against different levels of contour degradation is tested by simulation. Starting with a clean (without noise) set, L, it is assumed that the observed set, A, is a noisy version (with contour degradation) of L. The performance of the descriptors, when they are used to compare different shapes or shapes from a scene with models, is studied and compared with related descriptors based on the granulometric analysis of the original set, which are the closest previous alternative to our approach in the literature.