Fuzzy Edge Research Articles

A fuzzy edge detector driven telegraph total variation model for image despeckling

<p style='text-indent:20px;'>Speckle noise suppression is a challenging and crucial pre-processing stage for higher-level image analysis. In this work, a new attempt has been made using telegraph total variation equation and fuzzy set theory for image despeckling. The intuitionistic fuzzy divergence function has been used to distinguish between edges and noise. To the best of the authors' knowledge, most of the studies on the multiplicative speckle noise removal process focus only on diffusion-based filters, and little attention has been paid to the study of fuzzy set theory. The proposed approach enjoys the benefits of both telegraph total variation equation and fuzzy edge detector, which is robust to noise and preserves image structural details. Moreover, we establish the existence and uniqueness of weak solutions of a regularized version of the present system using the Schauder fixed point theorem. With the proposed technique, despeckling is carried out on natural, real synthetic aperture radar, and real ultrasound images. The experimental results computed by the suggested method are reported, which are found better in terms of noise elimination and detail/edge preservation, concerning the existing approaches.</p>

Types of fuzzy graph colouring and polynomial ideal theory

The graph colouring problem (GCP) is one of the most important optimisation problems in graph theory. In real life scenarios, many applications of graph colouring are fuzzy in nature. Fuzzy set and fuzzy graph can manage the uncertainty, associated with the information of a problem, where conventional mathematical models/graph may fail to reveal satisfactory result. To include those fuzzy properties in solving those types of problems, we have extended the various types of classical graph colouring methods to fuzzy graph colouring methods. In this study, we describe three basic types of fuzzy graph colouring methods namely, fuzzy vertex colouring, fuzzy edge colouring and fuzzy total colouring. We introduce a method to colour the vertices of the fuzzy graph using the polynomial ideal theory and find the fuzzy vertex chromatic number of the fuzzy graph. A practical example of scheduling committees meeting is given to demonstrate our proposed algorithm.

De-Speckling of SAR Images with Fuzzy Filters along with Altered Preserved Edge Values

In this research, the denoising of speckled SAR image has been done with fuzzy filters (ATMED, TMED, ATMAV & TMAV). SAR image or Synthetic Aperture Radar image consists of the informatics of ISW (Internal solitary waves). A new technique has been proposed which preserved the edge pixels by fuzzy edge detection method and then altered with the filtered image-pixels by fuzzy filtration for getting the denoised image. The comparative result shows that the proposed filter performs better than the other filtered results in terms of PSNR (41.61 dB), MAE (1.47), MSE (4.54) for TMAVxAPE & SSIM (81%) for ATMEDwAPE. The proposed method in this research shows better SSI (Spackle Suppression Index) value. Therefore the experimental result illustrates that the suggested fuzzy filter is much more capable of simultaneously protecting edges and suppressing speckle noise. This research will be beneficial to remove spackle noise from SAR images and can be used for remote sensing and mapping of surface area of earth.

Double-Norm Constrained Image Denoising Algorithm Based on Dictionary Learning Sparsity and FCM Structure Clustering

To solve the problem of image smoothness and fuzzy edge texture information after image denoising, proposed a new image denoising method based on dictionary learning. Firstly, the external cycling principal component analysis reduces the dimensions of image data while retaining the main data and constructing the learning dictionary. Secondly, used the fuzzy c-means structure clustering method internally to implement structural constraints on learning dictionary training, which considered the internal structure of image pixels. Then, the learning dictionary under the double constraints of sparse and structural clustering is obtained by internal and external iteration. Finally, the sparse representation coefficient and redundancy dictionary are obtained by the orthogonal matching pursuit method and alternate direction multiplier method, and the denoised images are estimated and updated according to sparse coding theory. Using the grayscale image from Set12 data set, color image from the CBSD68 data set, real noise from RENOIR data set, and texture image from USC-SIPI data set. The experimental results show that compared with the model-based algorithms (KSVD, ISKR, EPLL, NCSR, and LR-GSC) and the learning-based algorithms(DnCNN, IRCNN, and FFDNet), the proposed algorithm preserves the edge and texture information of the image better, and achieves better subjective visual effects and objective numerical results, especially for the image with complex structure and content, and the running time is much less than learning-based algorithms.

Key Points’ Location in Infrared Images of the Human Body Based on Mscf-ResNet

The human body generates infrared radiation through the thermal movement of molecules. Based on this phenomenon, infrared images of the human body are often used for monitoring and tracking. Among them, key point location on infrared images of the human body is an important technology in medical infrared image processing. However, the fuzzy edges, poor detail resolution, and uneven brightness distribution of the infrared image of the human body cause great difficulties in positioning. Therefore, how to improve the positioning accuracy of key points in human infrared images has become the main research direction. In this study, a multi-scale convolution fusion deep residual network (Mscf-ResNet) model is proposed for human body infrared image positioning. This model is based on the traditional ResNet, changing the single-scale convolution to multi-scale and fusing the information of different receptive fields, so that the extracted features are more abundant and the degradation problem, caused by the excessively deep network, is avoided. The experiments show that our proposed method has higher key point positioning accuracy than other methods. At the same time, because the network structure of this paper is too deep, there are too many parameters and a large volume of calculations. Therefore, a more lightweight network model is the direction of future research.

Поэтика Homo Economicus

Homo Economicus details Vogl's major project of betraying secular theodicy in modern economics. We get to know a new kind of man, his origin and future. It seems, Vogl concludes, that we have found the latest realization of homo economicus – a person who goes hand in hand with a new way of subjectivation. The compulsion to identity has become a compulsion to non-identity, otherness, uniqueness. The production of these so-called patchwork identities has become an economic program; a program that generates items with a flexible core and fuzzy edges – items that can be in demand on the market. With this figure, the history of homo economicus comes to an end; it gives way to a new type of people: a type that acts economically precisely by producing itself, its own subjectivity and spirituality. Vogl Joseph. Poetics of Homo Economicus // Continent. Issue 4.1. 2014: 95-104.

Matter Waves in the Talbot-Lau Interferometry

Particle paths, emitted from distributed sources and passing out through slits of two gratings, G0 and G1, up to detectors, have been computed in detail by the path integral method. The particles under consideration are fullerene molecules with a De Broglie wavelength equal to 5 pm. The slits are Gaussian functions that simulate fuzzy edges of the slits. Waves of the matter computed by this method show perfect interference patterns both between the gratings and behind the second grating. Coherent and non-coherent distributed particle sources reproducing the interference patterns are discussed in detail. Paraxial approximation results from removing the distributed sources onto innity. This approximation gives a wave function reproducing an exact copy of the Talbot carpet. PACS numbers: 03.75.-b, 03.75.Dg, 42.25.Hz

Синдром ятагана: подходы к диагностике и ведению

В статье представлен клинический случай синдрома ятагана у 3-летней девочки. Заболевание манифестировало в месячном возрасте с бледности, периорального цианоза и эпизода апноэ. Ребенок госпитализирован для дальнейшего обследования. У девочки наблюдался пери­оральный цианоз, SpO2 90 %, частота дыхательных движений 36/мин. Аускультативно в легких пуэрильное дыхание, ­ослабленное справа по передней поверхности грудной клетки. Сердечные тоны ритмичные, систолический шум 2/6 вверху левого края грудины и 4/6 в межлопаточной области слева. При эхокардиографии диагностированы некритическая коарктация аорты, дефект межпредсердной перегородки. При рентгенографии органов грудной клетки в области правого легочного поля интенсивная тень от верхушки до уровня V ребра с нечеткими краями. Тень сердца смещена вправо. Что у ребенка: декстрокардия, декстрапозиция, пневмония, ателектаз, тимомегалия? При бронхоскопии обнаружена обструкция бронхов верхней и средней доли правого легкого с густой мокротой белого цвета. Проведена санация. После бронхоскопии нормализовалась сатурация, но, несмотря на ликвидацию причины ателектаза, контрольная рентгенография оставалась без изменений. Проведена компьютерная томография органов грудной полости с контрастированием. У ребенка диагностирован синдром ятагана в сочетании с коарктацией аорты и аберрантной правой подключичной артерией. В возрасте 2,5 мес. выполнена баллонная дилатация коарктации аорты. В возрасте 2 лет проведена хирургическая коррекция — устранение аномального дренажа легочных вен и пластика дуги аорты. Интраоперационная анатомия: мезокардия, правые отделы сердца дилатированы, нижняя доля правого легкого гипоплазирована. Наша пациентка фактически не имела симптомов, характерных для синдрома ятагана, поскольку отсутствовали признаки сердечной недостаточности, девочка не болела респираторными заболеваниями. Диагноз в грудном возрасте был установлен благодаря эпизоду аспирации, но ателектаз и мезокардия помешали визуализации характерной рентгенологической картины синдрома ятагана. Поэтому полное исследование с использованием компьютерной томографии с контрастированием является обязательным для таких пациентов.

Evaluation of the airport runway flexible pavement macro-texture using digital image processing technique (DIPT)

ABSTRACT In this paper, we have explored and compared different digital image processing techniques on 2D Images of asphalt pavement for evaluating the macro-texture. Compared to the conventional tests, image processing provides a fast, reliable, and low-cost method with less computational complexity. The images were acquired using a digital camera on a fixed tripod at one angle (90°). We have studied three main digital image processing methods: Butterworth band-pass filter, edge pixels count and fuzzy logic edge detection. The derived parameters were compared with the measured mean texture depth using the sand patch test that was conducted at the same spots where images were captured in the airfield. In our analysis, both regression models of edge pixel count and fuzzy logic edge detection methods showed a good correlation (R 2 = 0.75; 0.76) with MTD data.

Edge Detection in Digital Images Using Guided L0 Smoothen Filter and Fuzzy Logic

Image segmentation is an important process in computer vision. Recently fuzzy logic based edge detection is heavily investigated as by changing the number of rules edge detection can be improved. However, due to large colour variations in the images false edges are detected and even using fuzzy rules they cannot be reduced significantly. These falsely detected edges can be controlled by using smoothen filter while controlling the degree of smoothness. This paper, presents fuzzy logic based edge detection mechanism while using Guided L0 smoothen filter for the smoothening of image under various degree of smoothens. Simulation results for edge detection is presented for Canny, Sobel, Fuzzy logic based edge detection and finally fuzzy logic edge detection with inclusion of L0 smoothen filter. The results are compared with classical and modern methods. Simulation is performed on Berkley Segmentation Database (BSD) and USC-SIPI Image Database while considering more than 100 images. The obtained F-measure is as high as 0.848.

Nanozymes: A clear definition with fuzzy edges

Nanozymes: A clear definition with fuzzy edges

Menger’s theorem for m-polar fuzzy graphs and application of m-polar fuzzy edges to road network

The main objective of this research article is to classify different types of m-polar fuzzy edges in an m-polar fuzzy graph by using the strength of connectedness between pairs of vertices. The identification of types of m-polar fuzzy edges, including α-strong m-polar fuzzy edges, β-strong m-polar fuzzy edges and δ-weak m-polar fuzzy edges proved to be very useful to completely determine the basic structure of m-polar fuzzy graph. We analyze types of m-polar fuzzy edges in strongest m-polar fuzzy path and m-polar fuzzy cycle. Further, we define various terms, including m-polar fuzzy cut-vertex, m-polar fuzzy bridge, strength reducing set of vertices and strength reducing set of edges. We highlight the difference between edge disjoint m-polar fuzzy path and internally disjoint m-polar fuzzy path from one vertex to another vertex in an m-polar fuzzy graph. We define strong size of an m-polar fuzzy graph. We then present the most celebrated result due to Karl Menger for m-polar fuzzy graphs and illustrate the vertex version of Menger’s theorem to find out the strongest m-polar fuzzy paths between affected and non-affected cities of a country due to an earthquake. Moreover, we discuss an application of types of m-polar fuzzy edges to determine traffic-accidental zones in a road network. Finally, a comparative analysis of our research work with existing techniques is presented to prove its applicability and effectiveness.

An Infrared Image Target Segmentation Based on Improved Threshold Method

In recent years, infrared images have been applied in more and more extensive fields and the current research of infrared image segmentation and recognition can’t satisfy the needs of practical engineering applications. The interference of various factors on infrared detectors result in the targets detected presenting the targets of low contrast, low signal-to-noise ratio (SNR) and fuzzy edges on the infrared image, thus increasing the difficulty of target detection and recognition; therefore, it is the key point to segment the target in an accurate and complete manner when it comes to infrared target detection and recognition and it has great importance and practical value to make in-depth research in this respect. Intelligent algorithms have paved a new way for infrared image segmentation. To achieve target detection, segmentation, recognition and tracking with infrared imaging infrared thermography technology mainly analyzes such features as the grayscale, location and contour information of both background and target of infrared image, segments the target from the background with the help of various tools, extracts the corresponding target features and then proceeds recognition and tracking. To seek the optimal threshold of an image can be seen as to find the optimum value of a confinement problem. As to seek the threshold requires much computation, to seek the threshold through intelligent algorithms is more accurate. This paper proposes an automatic segmentation method for infrared target image based on differential evolution (DE) algorithm and OTSU. This proposed method not only takes into consideration the grayscale information of the image, but also pays attention to the relevant information of neighborhood space to facilitate more accurate image segmentation. After determining the scope of the optimal threshold, it integrates DE’s ability of globally searching the optimal solution. This method can lower the operation time and improve the segmentation efficiency. The simulation experiment proves that this method is very effective.

Payload and quality augmentation using steganographic optimization technique based on edge detection

Information security is one of the most significant processes that must be taken into account when confidentially transferring information. This paper introduces a steganography technique using the edge detection method. It focused on three basic and important aspects’ payload, quality and security. Well-known edge detectors were used to generate as many edge pixels as possible to hide data and achieve the highest payload. The least significant bit (LSB) algorithm has been improved by extending the bits used to embed between 2-4 bits in smooth and sharp areas. To increase security, the transaction between the two parties is based on dividing the key and the cover image into several parts and agreeing on the type of edge detection.The experiments achieved the maximum load, for instance with a fuzzy edge detector, at first, embedding in 4 bitplanes if edge pixel and in 2 bitplanes if non-edge pixel, the peak signal-to-noise ratio (PSNR) increased from 43.580to 45.790. At second, embedding in 2 bitplanes if edge pixel, and in 4 bitplanes if non-edge pixel, the PSNR decreased between 38.433-41.593. The suggested scheme achieved a high pay load to embed in the cover image and according to human perception, it preserved the nature of the original image.

A Study On Distinct Fuzzy Colourings And Domination Parameters In Double Layered Fuzzy Graph

In Double Layered Fuzzy Graph, it is discussed that, the Fuzzy Vertex Colouring, Fuzzy Edge Colouring, Fuzzy Total Colouring and determined bounds for Fuzzy Chromatic Number, Fuzzy Edge Chromatic Number and Fuzzy Total Chromatic number. Also established some of the Domination parameters such as Fuzzy Dominator Chromatic Number, Inverse Domination, Connected Inverse Domination.

A contour-aware feature-merged network for liver segmentation based on shape prior knowledge

One primary challenge in liver segmentation is the fuzzy edge contour. Recently, fully convolutional neural networks (FCNs) have been widely used in liver segmentation because of their adequate feature extraction. Nevertheless, the context among liver slices is still ignored by FCN. To address this issue, we first propose a bidirectional convolutional long short-term memory (BiConvLSTM) to explore contextual information. Meanwhile, the attention gate (AG) is utilized to fuse high-dimensional information from BiConvLSTM to remove irrelevant features. Besides, Shape-Net network is proposed to extend the liver shape pattern by latent space information, which will contribute to reduce the interference of fuzzy boundaries. Finally, the improved active contour loss function with L2 norm acts as a feature constraint. Experimental results on public benchmark datasets show that the proposed method slightly outperforms other newly published methods and achieves good performance for liver segmentation.

Tissue harmonic versus contrast-enhanced harmonic endoscopic ultrasonography for the diagnosis of pancreatic tumors: Prospective multicenter study.

This prospective multicenter study aimed to assess and compare the accuracy of tissue harmonic endoscopic ultrasonography (TH-EUS) and contrast-enhanced harmonic endoscopic ultrasonography (CH-EUS) for differentiating pancreatic carcinoma from other pancreatic tumors. Consecutive patients with solid pancreatic tumors were prospectively enrolled between August 2013 and December 2014. To assess the accuracy of TH-EUS and CH-EUS, we compared four parameters of TH-EUS (fuzzy edge, irregular periphery, hypoechogenicity, and heterogeneous internal echogenicity) and four parameters of CH-EUS (hypoenhancement and heterogeneous enhancement in the early and late phases, respectively) to investigate which parameter of each method was most suitable to diagnose pancreatic carcinomas. Interobserver agreement and the diagnostic ability of pancreatic carcinoma using TH-EUS and CH-EUS were assessed and compared. A total of 204 patients were enrolled. For the diagnosis of pancreatic carcinoma, interobserver agreement by experts and nonexperts was 0.33-0.50 and 0.35-0.50 for TH-EUS, respectively, and 0.72-0.74 and 0.20-0.54 for CH-EUS, respectively. Irregular periphery was the most accurate diagnostic parameter among TH-EUS findings for differentiating pancreatic carcinomas, with sensitivity, specificity, and accuracy of 95.0%, 42.9%, and 78.9%, respectively. Late phase hypoenhancement was the most accurate diagnostic parameter among CH-EUS findings for differentiating pancreatic carcinomas, with sensitivity, specificity, and accuracy of 90.8%, 74.6%, and 85.8%, respectively. The accuracy of CH-EUS (late phase hypoenhancement) for diagnosis of pancreatic carcinoma was significantly higher than that of TH-EUS (irregular periphery) (p < 0.001). In comparison with TH-EUS, CH-EUS increased the diagnostic ability and reproducibility for the diagnosis of pancreatic carcinoma. UMIN (000011124).

Design and Analysis Of Underwater Imaging Application susing High Frequency Array

Underwater picture preparing has a few applications in the field of maritime exploration work and logical applications, for example, archaeology, geography, underwater ecological appraisal, laying of significant distance gas pipelines and correspondence joins across the mainlands which request geo-referential looking over of the maritime bed and prospection of complex task. The lowering of a camera underwater requires satisfactory lodging utilizing high frequency. The moving of the camera with the assistance from distant spot or face to face at the site is similarly a perplexing undertaking. In any case, the significant test is forced by underwater medium properties. Underwater dimness picture upgrade has acquired far and wide significance with the quick advancement of present day imaging gear. Notwithstanding, the difference upgrade of single underwater dim picture is a difficult errand for logical investigation and computational applications. Versatile differentiation upgrade calculations to determine the picture fluffiness was proposed to suit underwater pictures with shifting difference. Fuzzy edge held intensification strategy had the option to furnish better upgrade with very much protected edge data and improved differentiation in examination with the fuzzy enhancement technique. Acoustic imaging systems are likewise valuable for reviewing or inspecting objects when water turbidity blocks the utilization of closed circuit television or other optical methods for survey. Likewise, light is weakened with a horizontal distance of proliferation, decreasing the light energy arriving at the camera, accordingly bringing about a deficiency of the normal tone.

Coloring picture fuzzy graphs through their cuts and its computation

In a fuzzy set (FS), there is a concept of alpha-cuts of the FS for alpha in [0,1]. Further, this concept was extended into (alpha,delta)-cuts in an intuitionistic fuzzy set (IFS) for delta in [0,1]. One of the expansions of FS and IFS is the picture fuzzy set (PFS). Hence, the concept of (alpha,delta)-cuts was developed into (alpha,delta,beta)-cuts in a PFS where beta is an element of [0,1]. Since a picture fuzzy graph (PFG) consists of picture fuzzy vertex or edge sets or both of them, we have an idea to construct the notion of the (alpha,delta,beta)-cuts in a PFG. The steps used in this paper are developing theories and algorithms. The objectives in this research are to construct the concept of (alpha,delta,beta)-cuts in picture fuzzy graphs (PFGs), to construct the (alpha,delta,beta)-cuts coloring of PFGs, and to design an algorithm for finding the cut chromatic numbers of PFGs. The first result is a definition of the (alpha,delta,beta)-cut in picture fuzzy graphs (PFGs) where (alpha,delta,beta) are elements of a level set of the PFGs. Further, some properties of the cuts are proved. The second result is a concept of PFG coloring and the chromatic number of PFG based on the cuts. The third result is an algorithm to find the cuts and the chromatic numbers of PFGs. Finally, an evaluation of the algorithm is done through Matlab programming. This research could be used to solve some problems related to theories and applications of PFGs.

Tracking-based deep learning method for temporomandibular joint segmentation.

BackgroundThe shape, size, and surface information relating to the glenoid fossae and condyles in temporomandibular joints (TMJ) are essential for diagnosing and treating. Patients with TMJ disease often have surface abrasion which may cause fuzzy edges in computed tomography (CT) imaging, especially for low-dose CT, making TMJ segmentation more difficult.MethodsIn this paper, an automatic segmentation algorithm based on deep learning and post-processing was introduced. First, U-Net was used to divide images into 3 categories: glenoid fossae, condyles, and background. For structural fractures in these divided images, the internal force constraint of a snake model was used to replenish the integrity of the fracture boundary in a post-processing operation, and the initial boundary of the snake was obtained based on the basis of the tracking concept. A total of 206 cases of low-dose CT were used to verify the effectiveness of the algorithm, and such indicators as the Dice coefficient (DC) and mean surface distance (MSD) were used to evaluate the agreement between experimental results and the gold standard.ResultsThe proposed method is tested on a self-collected dataset. The results demonstrate that proposed method achieves state-of-the-art performance in terms of DCs = 0.92±0.03 (condyles) and 0.90±0.04 (glenoid fossae), and MSDs =0.20±0.19 mm (condyles) and 0.19±0.08 mm (glenoid fossae).ConclusionsThis study is the first to focus on the simultaneous segmentation of TMJ glenoid fossae and condyles. The proposed U-Net + tracking-based algorithm showed a relatively high segmentation efficiency, enabling it to achieve sought-after segmentation accuracy.