Adaptive Structuring Elements Research Articles

A Dynamic Model of Personal Adaptive Potential in the Conditions of Media Ecosystem

Relevance. The modern stage of information and communication technology development is attracting more and more researchers’ attention to the problem of risk causing factors that arise during the process of digitalization and mediatization. The category of adaptive potential that is used in various fields of socio-humanitarian knowledge appears to be an adequate conceptual instrument for media ecosystem risk problematization. However, in the framework of humanitarian and scientific digitalization discourse this concept is underdeveloped, and this fact determines the relevance of the theoretical construction(model) of personal adaptive potential in the conditions of media ecosystem, as well as the promising directions of its application.The purpose of the article is theoretical substantiation and construction of a dynamic model of an individual's adaptive potential to the risks of the media ecosystem.Objectives: to reveal the theoretical and methodological foundations of personal adaptive potential conceptualization in the conditions of media ecosystem; to specify the contents of adaptive potential levels; to describe adaptive potential dynamic interrelations with the corresponding parameters of the media ecosystem.Methodology. In the article sociological theories that focus on the socio-constructionist nature of modern technologies (actor-network theory, social construction of technology theory) are applied; as well as a number of basic general scientific methods (inductive and deductive methods, structural and functional approach). Special attention is paid to the methods of analogy and modeling, which made it possible to create a dynamic model of the adaptive potential of the individual in the media ecosystem and to lay the foundations for further operationalization of this category.Results. Basing on the results of the theoretical sources study, the author has constructed a dynamic model of personal adaptive potential. The article describes the value, dispositional and practical levels of adaptive potential, as well as their relationship with the corresponding parameters of the media ecosystem (institutional norms and habituses, patterns, affordances).Conclusions. The dynamic model constructed can be used as a methodological basis for theoretical and applied research. A promising direction is further operationalization and empirical verification of the model presented, as well as explication of adaptive potential structural elements.

Analysis of Adaptive Morphology for Visualization of Information Communication in Advertising Design

Abstract This paper uses adaptive morphology to construct an adaptive elliptic structure element that changes adaptively according to the image’s local neighborhood information. To calculate convolution, a Gaussian kernel with the outer product of the image gradient is used to achieve the diffusion of the linear matrix values of the initial matrix. The spatial distance of the pixels determines the size of the structural elements, and the pixel points that do not exceed the threshold are selected. The adaptive morphology method resulted in a 27.7 difficulty level for visual communication design, and the algorithm’s accuracy was 87.18%. Advertising design can break through dimensional limitations in visual space through adaptive morphology to generate interaction, indicating a path for development.

Evaluation and optimization of novel extraction algorithms for the automatic detection of atrial activations recorded within the pulmonary veins during atrial fibrillation

Background and objectiveThe automated detection of atrial activations (AAs) recorded from intracardiac electrograms (IEGMs) during atrial fibrillation (AF) is challenging considering their various amplitudes, morphologies and cycle length. Activation time estimation is further complicated by the constant changes in the IEGM active zones in complex and/or fractionated signals. We propose a new method which provides reliable automatic extraction of intracardiac AAs recorded within the pulmonary veins during AF and an accurate estimation of their local activation times.MethodsFirst, two recently developed algorithms were evaluated and optimized on 118 recordings of pulmonary vein IEGM taken from 35 patients undergoing ablation of persistent AF. The adaptive mathematical morphology algorithm (AMM) uses an adaptive structuring element to extract AAs based on their morphological features. The relative-energy algorithm (Rel-En) uses short- and long-term energies to enhance and detect the AAs in the IEGM signals. Second, following the AA extraction, the signal amplitude was weighted using statistics of the AA sequences in order to reduce over- and undersensing of the algorithms. The detection capacity of our algorithms was compared with manually annotated activations and with two previously developed algorithms based on the Teager–Kaiser energy operator and the AF cycle length iteration, respectively. Finally, a method based on the barycenter was developed to reduce artificial variations in the activation annotations of complex IEGM signals.ResultsThe best detection was achieved using Rel-En, yielding a false negative rate of 0.76% and a false positive rate of only 0.12% (total error rate 0.88%) against expert annotation. The post-processing further reduced the total error rate of the Rel-En algorithm by 70% (yielding to a final total error rate of 0.28%).ConclusionThe proposed method shows reliable detection and robust temporal annotation of AAs recorded within pulmonary veins in AF. The method has low computational cost and high robustness for automatic detection of AAs, which makes it a suitable approach for online use in a procedural context.

Construction Algorithm for Adaptive Morphological Structural Elements Based on the Fuzzy C-Means Clustering

Construction Algorithm for Adaptive Morphological Structural Elements Based on the Fuzzy C-Means Clustering

An adaptive morphology based segmentation technique for lung nodule detection in thoracic CT image

Lung cancer is one of the most life-threatening cancers mostly indicated by the presence of nodules in the lung. Doctors and radiological experts use High-Resolution Computed Tomography (HRCT) images for nodule detection and further decision making from visual inspection. Manual detection of lung nodules is a time-consuming process. Therefore, Computer-aided detection (CADe) systems have been developed for accurate nodule detection and segmentation. CADe-based systems assist radiologists to detect lung nodules with greater confidence and a lesser amount of time and have a significant impact on the accurate, uniform, and early-stage diagnosis of lung cancer. In this research work, an adaptive morphology-based segmentation technique (AMST) has been introduced by designing an adaptive morphological filter for improved segmentation of the lung nodule region. The adaptive morphological filter detects candidate nodule regions by employing adaptive structuring element (ASE) and at the same time improves nodule detection accuracy by reducing false positives (FPs) from the Computed Tomography (CT) slices. The detected nodule candidate regions are then processed for feature extraction. In this study, morphological, texture and intensity-based features have been used with support vector machine (SVM) classifier for lung nodule detection. The performance of the proposed framework has been evaluated by incorporating a 10-fold cross-validation technique on Lung Image Database Consortium-Image Database Resource Initiative (LIDC/IDRI) dataset and on a private dataset, collected from a consultant radiologist. It has been observed that the proposed automated computer-aided detection system has achieved overall classification performance indices with 94.88% sensitivity, 93.45% specificity and 94.27% detection accuracy with 1.8 FPs/scan on LIDC/IDRI dataset and 91.43% sensitivity, 90.45% specificity, 92.83% accuracy with 3.2 FPs/scan on a private dataset. The results show that the proposed CADe system presented in this paper outperforms the other state-of-the-art methods for automatic nodule detection from the HRCT image.

Intelligent Diagnosis of Rolling Bearing Fault Based on QFMAM

In order to solve the problem that the accuracy is not high when the traditional fuzzy morphological associative memories network (FMAM) is used to classify samples, based on quantum superposition and collapse, the quantum fuzzy morphological associative memories network (QFMAM) is proposed. In QFMAM, the structural element is constructed by the qubit system and the qubit probability represents corresponding membership degree to obtain adaptive structural elements. The samples are preprocessed first and then classified to ensure high classification accuracy. QFMAM, FMAM, support vector machine (SVM) and naive Bayes classifier (NBC) are used to classify simulation data and experimental signals of rolling bearings respectively. Comparing the performance of the four classifiers, it is obvious that the training time of QFMAM is far less than SVM and NBC, and the classification accuracy of the test samples by QFMAM is higher than that by the other three classifiers. QFMAM is very suitable for intelligent diagnosis of rolling bearing fault.

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.

Detection in seismic data using curvelet transform and tensor-based elliptical adaptive structuring elements

Channels are one of the vital issues in the exploration of oil and gas. They can be considered as reservoirs if they are filled with porous and permeable material and placed in a suitable position. A detailed study of the channels can help identify the sedimentation processes of the area, the intensity, and direction of the sea currents. Manual interpretation of the channels is difficult and requires skill. Therefore, in this paper, the adaptive curvelet and morphological gradient algorithm (ACMG) is used for the automatic interpretation of the channels. First, the morphological gradient is applied to extract the edges of the channels; then, the curvelet transform is used to increase the signal-to-noise ratio. The morphological top-hat operator extracts the local maxima of curvelet sub-bands. In this workflow, we applied an elliptical adaptive structuring element (EASE) based on Gradient structure tensor (GST). For the construction of the 2D GST, the horizontal and vertical gradients of the image were calculated by the first-order Gaussian derivative. The eigenvalue decomposition of each structure tensor can provide an estimate of the direction and anisotropy rate of the image objects. Hence, parameters of the elliptical structuring element are obtained by eigenvalue decomposition of the GST. To evaluate ACMG, we compared the final output with the results of the non-adaptive curvelet and morphological gradient algorithm (CMG). Also, we compared it with the common edge-detectors such as Canny, Sobel, Laplacian of Gaussian, and similarity attribute. The comparison shows that both CMG and ACMG well extracted the edge of the channels with the higher signal-to-noise ratio.

Hardware Implementation for Haze Removal With Adaptive Filtering

Haze removal is useful in computational photography and computer vision applications. Single image haze removal is still a challenge for real-time embedded systems. In general, dark channel prior approach can effectively remove haze from a single image. However, the halo effect is the major problem of dehazing. In this study, adaptive filtering, which is easy for VLSI design, is introduced to refine the transmission map and improve the result of haze removal. A hardware architecture for our haze removal method is proposed to achieve the real-time requirement. A specific hardware component is designed to compute lengths of the paths and adaptive structuring element (ASE). The hardware architecture for haze removal is implemented in 180 nm CMOS technology and occupies 5.46 mm 2 with 143 K bit on-chip memory. The design can operate at 177MHz and support for HD (1280×720) 192 frame/s. Experiment results show that the hardware implementation is suitable in embedded system for real-time HD applications.

Adaptive top-hat filter based on quantum genetic algorithm for infrared small target detection

With the development of infrared technology, infrared small targets detection has attracted great interest of researchers. Top-hat filter is one of widely used methods for detecting infrared small target, and the structure elements have great influence on the performance of detection. The structure elements are desired to be adjusted adaptively. To this end, an adaptive structure elements optimization method based on quantum genetic algorithm (QGA) is introduced, and the convergence of QGA reveals the effectiveness of QGA. Experimental results show that the proposed adaptive top-hat filter based on QGA can achieve more stable infrared small target detection performance compared with the traditional top-hat filter.

Morphology operators construction by adaptive elliptical structuring elements based on nonlinear structure tensor

As the linear structure tensor is tending to inaccurately or even wrong estimate the gradient direction of a gray-level image, we present a novel algorithm to construct adaptive elliptical structuring elements via estimating the local anisotropy of an image based on the nonlinear structure tensor. Erosion, dilation, opening, closing and Hit-or-Miss transform are redefined according to the presented structuring elements, which have been applied to some representative images. The processed results and the quantitative analysis show that the novel morphological operators have more advantages in structure adaptation, corner protection, filtering and targets extraction than the others.

Adaptive Unsymmetrical Trim-Based Morphological Filter for High-Density Impulse Noise Removal

The modified decision-based unsymmetrical trimmed median filter (MDBUTMF), which is an efficient tool for restoring images corrupted with high-density impulse noise, is only effective for certain types of images. This is because the size of the selected window is fixed and some of the center pixels are replaced by a mean value of pixels in the window. To address these issues, this paper proposes an adaptive unsymmetrical trim-based morphological filter. Firstly, a strict extremum estimation approach is used, in order to decide whether the pixel to be processed belongs to a monochrome or non-monochrome area. Then, the center pixel is replaced by a median value of pixels in a window for the monochrome area. Secondly, a relaxed extremum estimation approach is used to control the size of structuring elements. Then an adaptive structuring element is obtained and the center pixel is replaced by the output of constrained morphological operators, i.e., the minimum or maximum of pixels in a trimmed structuring element. Our experimental results show that the proposed filter is more robust and practical than the MDBUTMF. Moreover, the proposed filter provides a preferable performance compared to the existing median filters and vector median filters for high-density impulse noise removal.

Extraction of QRS fiducial points from the ECG using adaptive mathematical morphology

QRS complex detection in the electrocardiogram (ECG) has been extensively investigated over the last two decades. Still, some issues remain pending due to the diversity of QRS complex shapes and various perturbations, notably baseline drift. This is especially true for ECG signals acquired using wearable devices. Our study aims at extracting QRS complexes and their fiducial points using Mathematical Morphology (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 for a more robust and precise detection. The MIT-BIH arrhythmia and Physionet QT databases were respectively used for assessing the detection performance of R-waves and other fiducial points. Furthermore, the proposed method was evaluated on a wearable-device dataset of ECGs during vigorous exercises. Results show comparable or better performance than the state-of-the-art with a 99.87% sensitivity and 0.22% detection error rate for the MIT-BIH arrhythmia database. Efficient extraction of QRS fiducial points was achieved against the Physionet QT database. On the wearable-device dataset, an improvement of more than 10% in QRS complex detection rate compared to classic approaches was obtained.

Adaptive Morphological Filtering Method for Structural Fusion Restoration of Hyperspectral Images

Recovering hyperspectral image (HSI) from mixed noise degradation is a challenging and promising theme in remote sensing, particularly when stripes and deadlines exist in several contiguous bands. This paper proposes a HSI's restoration method making use of adaptive morphological filtering (AMF) and fusing structure information of an auxiliary color image. An adaptive structuring element (ASE) indicating morphological features of each pixel is generated through information fusion, to simultaneously remove the mixed noise and preserve fine spatial structures. This key technology contains three main steps. First, edges are extracted from the auxiliary image exploiting its color information; then, an edge-constraint growing algorithm is used to generate the clustering kernel; finally, the ASE is obtained via goal-guided k-means clustering. The ASE has extensive application value, for it can be an enhancing module for most filters-based restoration methods, to mitigate the structural damage due to the fixed mask. Among these methods, Gaussian filter for preprocessing and majority voting for postprocessing are introduced in this paper as representatives. In addition, the auxiliary image can be both visible image of multisensor and false RGB component of the undamaged bands of the HSI, so it is relatively available. Experiments on simulated and real data sets show obvious effects on denoising and destriping both subjectively and objectively. The advantage of ASE on structure details preserving, compared to conventional approaches, is clearly demonstrated. The application value of the proposed restoration frame and ASE is further proved through the decision-level postprocessing experiments.

An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images.

Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorioretinal diseases, which can cause loss of central vision. In this paper, an automated framework is proposed to segment serous PED in SD-OCT images. The proposed framework consists of four main steps: first, a multi-scale graph search method is applied to segment abnormal retinal layers; second, an effective AdaBoost method is applied to refine the initial segmented regions based on 62 extracted features; third, a shape-constrained graph cut method is applied to segment serous PED, in which the foreground and background seeds are obtained automatically; finally, an adaptive structure elements based morphology method is applied to remove false positive segmented regions. The proposed framework was tested on 25 SD-OCT volumes from 25 patients diagnosed with serous PED. The average true positive volume fraction (TPVF), false positive volume fraction (FPVF), dice similarity coefficient (DSC) and positive predictive value (PPV) are 90.08%, 0.22%, 91.20% and 92.62%, respectively. The proposed framework can provide clinicians with accurate quantitative information, including shape, size and position of the PED region, which can assist clinical diagnosis and treatment.

Infrared Target Extraction Using Weighted Information Entropy and Adaptive Opening Filter

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.

Text string detection for loosely constructed characters with arbitrary orientations

Text in a scene provides vital information of its contents. With the increasing popularity of vision systems, detecting general text in images becomes a critical yet challenging task. Most existing methods have focused on extracting neatly arranged text string for compactly constructed characters. Motivated by the need to consider the widely varying forms of scene text, we propose a stroke-based text detection method which detects arbitrary orientations text strings with loosely constructed characters in images. Our approach employs result of stroke width transform (SWT) as basic stroke candidates. These candidates are then merged using adaptive structuring elements to generate compactly constructed characters. Individual characters are chained using k-nearest neighbors algorithm to identify arbitrary orientations text strings, which are subsequently separated into words if necessary. To better evaluate our system and compare it with other competing algorithms, we generate a new dataset, which includes various characters and text strings in diverse scenes. Experiments on ICDAR datasets and the proposed dataset demonstrate that our approach compares favorably with the state-of-the-art algorithms when handling arbitrary orientations text strings and achieves significantly enhanced performance on loosely constructed characters in scenes.

Extracting Ground-Level DEM From SRTM DEM in Forest Environments Based on Mathematical Morphology

The demands for bare-earth or ground-level digital elevation models (DEMs) are significant for various environmental and ecological studies. As one of the most widely used global DEMs, the Shuttle Radar Topography Mission (SRTM) DEM is available to the public. However, the SRTM DEM is not a bare-earth DEM because it includes man-made structures and vegetation. The objective of this paper is to develop a mathematical morphology-based approach to generate ground-level DEM (GLD) from the SRTM DEM in forest environments. The proposed algorithm is implemented as follows. First, an initial GLD is derived from the SRTM DEM by using morphological operations with a single structuring element. Second, homogeneous forest patches are generated by applying watershed segmentation to pseudocanopy height (PCH) that is obtained by subtracting the initial GLD from the SRTM DEM. Based on segmented patches, a refined GLD is derived by using morphological operations with adaptive structuring elements of different sizes. Third, PCH is updated with the refined GLD and then resegmented into forest patches, from which a final GLD is obtained by subtracting the updated mean PCH from the SRTM DEM. Finally, bare-earth DEMs from the National Elevation Data Set (NED) are used to validate the extracted GLD. The results show that the root mean square error of the final GLD compared with the NED is significantly reduced for two study sites. This type of GLD would be applicable to large-scale environmental studies where accurate topographical information is not available.

Removal of high-density impulse noise based on switching morphology-mean filter

In this paper, an effective switching morphology-mean filter is proposed to remove impulse noise from digital images. The proposed method consists of two stages. The first stage is to detect the impulse noise in an image. The second stage is to construct adaptive structuring elements and remove the impulse noise. In this stage, the “noise-pixels” are filtered out by using the morphology-mean filter, and the “noise-free pixels” are copied directly to the output image. The proposed filter uses the window of size 3×3pixels. Simulation results show that the proposed method outperforms some existing algorithms for high-density impulse noise, both in vision and quantitative measurements. Moreover, our method can preserve effectively the details in an image, and has less processing time.