Fuzzy Edge Research Articles

An Application of Interval Valued Fuzzy Matrix in Modeling Clinical Waste Incineration Process

This paper exploits an interval valued fuzzy matrix in representing combustion process of clinical waste incineration system which previously modelled by using Fuzzy Autocatalytic Set (FACS). We defined a membership degree of interval-valued fuzzy edge connectivity using concept of infimum and supremum and proposed a mapping from FACS of clinical waste incineration process to an interval valued fuzzy matrix of FACS. Basic characteristics of the matrix are observed and some proven theorems are derived.

Research on Digital Economy and Human Resources Based on Fuzzy Clustering and Edge Computing

The internationalization of the digital economy is an important part of the development stage of the digital economy. How to improve the internationalization level of the digital economy, promote the international competitiveness of digital enterprises, and accelerate the pace of digital enterprises “going out” has become the current digital economy research field problem that needs to be solved urgently. Aiming at the current deficiencies in the development of the digital economy, this paper combines edge computing and fuzzy clustering technology to carry out research on digital economy and human resources. First of all, through the clustering analysis of the factors of digital economy and human resources, the weight of each factor is obtained. Secondly, edge computing technology is used in the architectural design of digital economy and human resources research. Finally, the simulation test analysis verifies the efficiency of the digital economy and human resources architecture based on fuzzy clustering and edge computing established in this paper. The research is helpful for the development of the analysis of digital economy and human resources.

MFANet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover

Detailed information regarding land utilization/cover is a valuable resource in various fields. In recent years, remote sensing images, especially aerial images, have become higher in resolution and larger span in time and space, and the phenomenon that the objects in an identical category may yield a different spectrum would lead to the fact that relying on spectral features only is often insufficient to accurately segment the target objects. In convolutional neural networks, down-sampling operations are usually used to extract abstract semantic features, which leads to loss of details and fuzzy edges. To solve these problems, the paper proposes a Multi-level Feature Aggregation Network (MFANet), which is improved in two aspects: deep feature extraction and up-sampling feature fusion. Firstly, the proposed Channel Feature Compression module extracts the deep features and filters the redundant channel information from the backbone to optimize the learned context. Secondly, the proposed Multi-level Feature Aggregation Upsample module nestedly uses the idea that high-level features provide guidance information for low-level features, which is of great significance for positioning the restoration of high-resolution remote sensing images. Finally, the proposed Channel Ladder Refinement module is used to refine the restored high-resolution feature maps. Experimental results show that the proposed method achieves state-of-the-art performance 86.45% mean IOU on LandCover dataset.

Image Edge Detection: A New Approach Based on Fuzzy Entropy and Fuzzy Divergence

In image pre-processing, edge detection is a non-trivial task. Sometimes, images are affected by vagueness so that the edges of objects are difficult to distinguish. Hence, the usual edge-detecting operators can give unreliable results, thus necessitating the use of fuzzy procedures. In literature, Chaira and Ray approach is a popular technique for fuzzy edge detection in which fuzzy divergence formulation is exploited. However, this approach does not specify the threshold technique must be applied. Then, in this work, starting from Chairy and Ray procedure, we present a new fuzzy edge detector based on both fuzzy divergence (thought and proved to be a distance) and fuzzy entropy minimization for the thresholding sub-step in gray-scale images. Eddy currents, thermal infrared, and electrospinning images were used to test the proposed procedure after their fuzzification by a suitable adaptive S-shaped fuzzy membership function. Moreover, the fuzziness content of each image has been quantified by new specific indices proposed here and formulated in terms of fuzzy divergence. The results have been evaluated by suitable assessment metrics here formulated and are considered to be encouraging when qualitatively and quantitatively compared with those obtained by some well-known I- and II-order edge detectors.

Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain* *Project supported by the National Natural Science Foundation of China (Grant No. 11471004), and the Key Research and Development Program of Shaanxi Province, China (Grant No. 2018SF-251).

To solve the problem that the magnetic resonance (MR) image has weak boundaries, large amount of information, and low signal-to-noise ratio, we propose an image segmentation method based on the multi-resolution Markov random field (MRMRF) model. The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales. The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm, and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation. The results are then segmented by the improved MRMRF model. In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model, it is proposed to introduce variable weight parameters in the segmentation process of each scale. Furthermore, the final segmentation results are optimized. We name this algorithm the variable-weight multi-resolution Markov random field (VWMRMRF). The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness, and can accurately and stably achieve low signal-to-noise ratio, weak boundary MR image segmentation.

Dental Images' Segmentation Using Threshold Connected Component Analysis.

Recent advances in medical imaging analysis, especially the use of deep learning, are helping to identify, detect, classify, and quantify patterns in radiographs. At the center of these advances is the ability to explore hierarchical feature representations learned from data. Deep learning is invaluably becoming the most sought out technique, leading to enhanced performance in analysis of medical applications and systems. Deep learning techniques have achieved great performance results in dental image segmentation. Segmentation of dental radiographs is a crucial step that helps the dentist to diagnose dental caries. The performance of these deep networks is however restrained by various challenging features of dental carious lesions. Segmentation of dental images becomes difficult due to a vast variety in topologies, intricacies of medical structures, and poor image qualities caused by conditions such as low contrast, noise, irregular, and fuzzy edges borders, which result in unsuccessful segmentation. The dental segmentation method used is based on thresholding and connected component analysis. Images are preprocessed using the Gaussian blur filter to remove noise and corrupted pixels. Images are then enhanced using erosion and dilation morphology operations. Finally, segmentation is done through thresholding, and connected components are identified to extract the Region of Interest (ROI) of the teeth. The method was evaluated on an augmented dataset of 11,114 dental images. It was trained with 10 090 training set images and tested on 1024 testing set images. The proposed method gave results of 93% for both precision and recall values, respectively.

Encoder-Decoder Structure With the Feature Pyramid for Depth Estimation From a Single Image

We address the problem of depth estimation from a single monocular image in the paper. Depth estimation from a single image is an ill-posed and inherently ambiguous problem. In the paper, we propose an encoder-decoder structure with the feature pyramid to predict the depth map from a single RGB image. More specifically, the feature pyramid is used to detect objects of different scales in the image. The encoder structure aims to extract the most representative information from the original image through a series of convolution operations and to reduce the resolution of the input image. We adopt Res2-50 as the encoder to extract important features. The decoder section uses a novel upsampling structure to improve the output resolution. Then, we also propose a novel loss function that adds gradient loss and surface normal loss to the depth loss, which can predict not only the global depth but also the depth of fuzzy edges and small objects. Additionally, we use Adam as our optimization function to optimize our network and speed up convergence. Our extensive experimental evaluation proves the efficiency and effectiveness of the method, which is competitive with previous methods on the Make3D dataset and outperforms state-of-the-art methods on the NYU Depth v2 dataset.

Research on blurred edge information segmentation of image based on computer vision

In the process of image fuzzy edge information segmentation by traditional methods, the segmentation effect is not ideal, the completion time is long and the accuracy is low. A fuzzy edge information segmentation method based on computer vision is proposed. After image denoising, image sharpening is carried out to extract image fuzzy edge information features. By designing a super-pixel grid, the pixels of the fuzzy edge information features of the image are matched, the inverse tensor information of the fuzzy edge of the image is analysed, and the multi-threshold values are normalised. The processing results are overlaid on the single object in the image to realise the fuzzy edge information segmentation. Experimental results show that the proposed method has better segmentation effect, shorter completion time and higher accuracy, and is of practical significance.

FUZZY EDGE IMAGE MATCHING ALGORITHM FOR SQUID SPECIES IDENTIFICATION

Abstract: The image features play important role in matching system. The effectiveness of these Squid species features depends on the global features. The identification of Squid species requires information of their morphology. Body shape is very useful to characterize the one species to another species. In Shape extraction, edge detection is an important aspect. Edge is an important visual feature and it represents visual information with a limited number of pixels. While considering the morphology of Squid, it can have uncertainty due to climatic conditions. Hence, in this study feature extraction is done by fuzzy edge map. In this paper we proposed Fuzzy Image Edge Image Matching Algorithm (FEIMA) for Squid species identification. Similarity metric is used for matching of query and the candidate images in the database and it finally displays the class of species. The proposed algorithm performance is calculated by using Average of precision and recall.

Prediction of Tetralogy of Fallot using Fuzzy Clustering

Background: Congenital Heart Disease is one of the abnormalities in your heart's structure. To predict the tetralogy of fallot in a heart is a difficult task. Cluster is the collection of data objects, which are similar to one another within the same group and are different from the objects in the other clusters. To detect the edges, the clustering mechanism improve its accuracy by using segmentation, Colour space conversion of an image implemented in Fuzzy c-Means with Edge and Local Information. Objective: To predict the tetralogy of fallot in a heart, the clustering mechanism is used. Fuzzy c-Means with Edge and Local Information gives an accuracy to detect the edges of a fallot to identify the congential heart disease in an efficient way. Methods: One of the finest image clustering methods, called as Fuzzy c-Means with Edge and Local Information which will introduce the weights for a pixel value to increase the edge detection accuracy value. It will identify the pixel value within its local neighbor windows to improve the exactness. For evaluation , the Adjusted rand index metrics used to achieve the accurate measurement. Results: The cluster metrics Adjusted rand index and jaccard index are used to evaluate the Fuzzy c- Means with Edge and Local Information. It gives an accurate results to identify the edges. By evaluating the clustering technique, the Adjusted Rand index, jaccard index gives the accurate values of 0.2, 0.6363, and 0.8333 compared to other clustering methods. Conclusion: Tetralogy of fallot accurately identified and gives the better performance to detect the edges. And also it will be useful to identify more defects in various heart diseases in a accurate manner. Fuzzy c-Means with Edge and Local Information and Gray level Co-occurrence matrix are more promising than other Clustering Techniques.

Are Borders Inside or Outside?

When a boat disappears over the horizon, does a distant observer detect the last moment in which the boat is visible, or the first moment in which the boat is not visible? This apparently ludicrous way of reasoning, heritage of long-lasting medieval debates on decision limit problems, paves the way to sophisticated contemporary debates concerning the methodological core of mathematics, physics and biology. These ancient, logically-framed conundrums throw us into the realm of bounded objects with fuzzy edges, where our mind fails to provide responses to plain questions such as: given a closed curve with a boundary (say, a cellular membrane) how do you recognize what is internal and what is external? We show how the choice of an alternative instead of another is not arbitrary, rather points towards entirely different ontological, philosophical and physical commitments. This paves the way to novel interpretations and operational approaches to challenging issues such as black hole singularities, continuous time in quantum dynamics, chaotic nonlinear paths, logarithmic plots, demarcation of living beings. In the sceptical reign where judgements seem to be suspended forever, the contemporary scientist stands for a sort of God equipped with infinite power who is utterly free to dictate the rules of the experimental settings.

Research on Edge Detection Method Based on Improved HED Network

Aiming at the problem of rough and fuzzy edges generated by the current edge detection technology based on HED network, an improved edge detection method for HED network is proposed. First, the useful information captured by each convolutional layer of HED becomes rougher as the size of the acceptance field increases. The improved HED network makes use of all the information of the convolution layer to capture more targets in a larger range, or make the local boundaries of the targets possible. The improved HED makes full use of the multi-scale and multi-level information of the target to obtain high-precision and high-quality edge maps, which lays a good foundation for image segmentation.

Mass smart detection for breast cancer diagnosis in mammographic images

Background: Breast cancer is one of the most common types of cancer among women. Mammography is the most standard method for diagnosing breast cancer, which can be minimize the human error using computer systems of human error. Methods: In this paper, using the image processing techniques, the mass was detected and identified in the photograph and then intelligent system outlined its margin After removing the image noise, using the fuzzy inference system, the fuzzy edge improvement has been applied and then using the coordinate logic filter, the mass areas have been detected and shown in the image. Results: The proposed smart system have p <0.001 for the correct diagnosis compared to the human diagnostic methods. Conclusion: The smart system results have been tested on 322 MIAS database images. In this database, 120 cases have benign and malignant tumors and 202 are healthy. The smart system was able to detect 115 cancer cases (true positive) and 190 healthy people (true negative) correctly. The number of false positive and false negative are 12 and 5, respectively. Therefore, the accuracy of the smart system for the database is 95%, and the sensitivity and specificity are 96% and 94%, respectively.

Exploration of the Mesoscopic Failure Process of Granite Based on a PDE Method

The meso-structure of rock influences its macro-mechanical properties. In traditional digital image processing, the filter operator causes fuzzy edges. There is a lack of image processing methods which can conveniently, and quickly, obtain a continuous, smooth, and closed curve with a clear boundary. In this study, a Chan-Vese (C-V) module of the partial differential equation (PDE) method was used to realise the three-phase segmentation of granite. Then, the AUTOLISP language was adopted to allow automatic line connection of the discrete boundary points in the digital image. The meso-scale model was implemented into numerical calculations to explore the evolution of a meso-crack. The crack originated from the stress concentration resulting from the uncoordinated deformation of the non-homogeneous materials under a Brazilian splitting test. The crack first appeared inside the materials with weak mechanical properties along the main diagonal of specimens under uniaxial compression. A stress concentration first appeared on the quartz-mica contact surface under triaxial compression. The tensile stress enveloped the weaker mica and feldspar as the load increased.

An Infrared Defect Sizing Method Based on Enhanced Phase Images.

Infrared thermography (IRT) is a full-field, contactless technique that has been widely used for nondestructive evaluation of structural materials due to many advantages. One of the major limitations of IRT is the fuzzy edge and low contrast in the inspected images—as well as the cost of the system. An efficient image post-processing with an affordable and portable device is of great interest to the engineering society. In this study, a convenient and economical inspection system using common halogen lamps was constructed. The corresponding image-processing scheme, which includes Fourier phase analysis and specific image enhancement was developed to identify defects with sharp and clear edges and good contrast. This system was applied to localized of defects in glass-fiber-reinforced composite panels. The results showed that defects with an effective diameter as small as 5 mm can be detected with excellent image quality. As a conclusion, the developed system provides an economic alternative to traditional infrared thermography which is able to identify defects with good qualities.

Analysis and improvement of image segmentation algorithm based on fuzzy edge compensation

In order to solve the problems of mosaics and fuzzy details in traditional image interpolation and magnification algorithms, an image magnification algorithm based on compensation item edge segmentation is proposed. According to the boundary degree a

Fuzzy edge connectivity and fuzzy local edge connectivity with applications to communication networks

The authors (Mathew and Sunitha (2010) [9]) introduced the concept of fuzzy arc cuts for improving the definition of the so-called cut-sets proposed by Yeh and Bang, both generalizing edge cuts from classic graph theory to the fuzzy settings. They argued that non-strong edges need not be considered in a fuzzy arc cut as such edges would not change the strength of connectedness between any vertices. However, we give a counterexample in the paper, which illustrates that this viewpoint may contain some flaws. Based on this observation, a more general definition of fuzzy arc cuts (called fuzzy edge cuts) is given. Moreover, we define fuzzy edge connectivity with two parameters, this definition allows us to talk about edge connectivity more precisely. The concepts of fuzzy local edge cuts and fuzzy local edge connectivity are also introduced. We investigate the relations between fuzzy edge cuts and fuzzy local edge cuts. A characterization for the weight of a minimal fuzzy local edge cut is obtained. Finally, we provide some algorithms to compute fuzzy edge connectivity and fuzzy local edge connectivity of special graphs. Besides, applications of two connectivity parameters in communication networks are presented.

Squid Species Matching using Fuzzy Edge Based Algorithm

In the area of Commercial species identification, Squids species identification is significant because Squids plays an important role in Marine food chain. The identification of Squid species requires information about their morphometric features. Body shape feature is one of the important morphometric features for Squids. Hence, we consider only shape feature of Squid. Edge detection is an important technique to extract the shape feature for Squid images. Squid images contains uncertainty because of the problems occurs in the data acquisition and its complex structure. Hence, to avoid above mentioned uncertainties occurs in the Squid images consider Fuzzy edge map. In this work Fuzzy Edge Based Retrieval Algorithm is proposed for the query based Squid image retrieval from Squid’s database. In the process of Fuzzy Edge Based Retrieval Algorithm, first Fuzzy Edge map is constructed for Squid images later the Euclidian distance similarity measure performs between Query image and the candidate images in the Squids database. Based on the similarity metric the relevant Squid images are matched with query image are retrieved. The performance of proposed algorithm analysed with precision recall graphs.

A Hybrid Demosaicking Algorithm for Area Scan Industrial Camera Based on Fuzzy Edge Strength and Residual Interpolation

Area scan cameras are widely used in industrial manufacturing for quality controlling, production monitoring, or defect inspection. The area scan cameras are normally integrated in a single sensor covered by the Bayer color filter array for RGB signal capturing, and the raw image with one channel for each pixel is obtained in one exposure. Therefore, it is significant to reconstruct the other two channels at each pixel which is called demosaicking. In this article, one hybrid demosaicking algorithm is proposed based on fuzzy edge strength and residual interpolation (RI) method. We first demosaic the G channel with one edge-directed filter and refine the high textured details by using fuzzy edge strength interpolation. Then, the RI is employed to calculate R and B channels, where G is used as the guide image for computing residuals. In the experiment, we evaluate our proposed method with nine prevalent interpolation-based algorithms on the IMAX and Kodak image datasets. The experimental results demonstrate that the proposed algorithm outperform the others by synthesizing the performance in peak signal-to-noise ratio, structural similarity, visual comparison, and computational time.

Multi-Directional Reconstruction Algorithm for Panoramic Camera

of view. It can be applied in virtual reality, smart homes and other fields as well. A multi-directional reconstruction algorithm for panoramic camera is proposed in this paper according to the imaging principle of dome camera, as the distortion inevitably exists in the captured panorama. First, parameters of a panoramic image are calculated. Then, a weighting operator with location information is introduced to solve the problem of rough edges by taking full advantage of pixels. Six directions of the mapping model are built, which include up, down, left, right, front and back, according to the correspondence between cylinder and spherical coordinates. Finally, multi-directional image reconstruction can be realized. Various experiments are performed in panoramas (1024×1024) with 30 different shooting scenes. Results show that the azimuth image can be reconstructed quickly and accurately. The fuzzy edge can be alleviated effectively. The rate of pixel utilization can reach 84%, and it is 33% higher than the direct mapping algorithm. Large scale distortion is also further studied.