Fuzzy Image Processing Research Articles

Revolutionizing Precision Agriculture with Drone-Based Imaging and Fuzzy Intelligent Algorithms

This paper proposes a clear understanding of drone based agricultural image processing techniques using fuzzy intelligent algorithm. This procedure is used for drone-based agricultural monitoring through intelligent calculations by various images acquired and trained from the field. The objective is to upgrade the exactness and proficiency of crop yield and the accuracy in agriculture. The proposed methods use high quality images caught by drones to remove crop-related infections and evaluate their wellbeing status. The fuzzy based calculation is applied to characterize the harvest wellbeing status and produce a choice to guide and assist the farmers of various regions to concentrate the area of infection based on the images captured from drones. The projected approach is assessed utilizing the information and the consideration and appropriate recommendation can be made after careful evaluation of infected region in the field. Graphical User Interface (GUI) is developed using fuzzy based algorithm which can segment the infectious leaves and classify the type of diseases and check the healthiness of the leaves. Results show that the proposed approach accomplishes higher exactness and improved effectiveness in distinguishing crop wellbeing status in comparable with existing the procedures. This study shows the capability of integrating drone-based agriculture with fuzzy intelligent image processing tool which makes efficient for farmers to improve the quality of crops, attain greater yield and to develop the nation with drone-based agribusiness.

Detecting Dehydration Based on Urine Color Using Fuzzy Logic Image Processing and Regulating Water Intake with an Automatic Water Pump According to Dehydration Level Using an IoT-Based

Dehydration is a condition where the body lacks the fluids it needs to carry out its functions optimally. Dehydration can cause various health problems, including decreased mental and physical performance, and can even cause death if not treated immediately. Therefore, it is important to be able to detect and treat dehydration early. One way to detect dehydration is through urine color analysis. Urine that is darker than normal can be a sign of dehydration. The classification of dehydration level according to urine color is as follows: 1-2: Hydrated, 3-4: Mildly dehydrated, 5-6: Dehydrated, 7-8: Very dehydrated. This research aims to develop an IoT-based dehydration detection system that can detect the level of dehydration in a person based on urine color and regulate water intake automatically using a water pump. The novelty of this research is the method of integrating drinking water intake with dehydration detection based on real-time urine color based on IoT using the Fuzzy Logic method. The results of this research are used by the Jember State Polytechnic TeFa Nutrition Care Center (NCC) in serving patients. The methodology used in this research is Fuzzy Logic image processing to process urine color data and determine a person's level of dehydration. After carrying out this research, the following conclusions were obtained: Based on the literature study in this research, 8 levels of hydration status according to NSW Health were obtained, then from this literature a method was obtained to measure a person's hydration based on urine color using image processing using the Fuzzy Logic method.

A Controlling Traffic Light system using Fuzzy logic

Automatic control systems are a new advanced system uses computers and hardware controllers to act as a driving system. Increasing in cars numbers while we have the same infrastructure caused a large traffic problem so we need automatic adaptive controllers to control the open and close tasks of traffic light system. Fuzzy Logic is a conversation from classical logic with values of 0 for false and 1 for true to a range of values between 0 and 1.
 This study aims to create an adaptive traffic control system using fuzzy logic and image processing, we have a camera on each traffic light to calculate the number of cars there and then we feed the number of cars to a fuzzy logic system to decide which traffic light must become on

AUTOMATED FOREST FIRE DETECTION USING FUZZY LOGIC BASED IMAGE PROCESSING METHOD

Wildfires and the extent of the damage and destruction they leave behind are well known. Due to the limitations of manual methods for monitoring fires, there is an urgent need to develop automated methods for them. In recent years, there has been significant development of vision systems for fire detection. This article presents a method for identifying fire and smoke using fuzzy logic image processing to achieve efficient and early detection of forest fires. The fuzzy logic-based fire detection method is designed to detect and segment areas of flame and smoke in visual images captured by a camera on board an unmanned aerial vehicle (UAV). The model has been tested on a wide range of aerial images containing areas of fire and smoke and has shown good performance up to 95 % accu- racy.

Retracted: Partial Differential Equation Noise Reduction Model and Fuzzy Image Processing in Optimal Application of Sports Dance Exercise Training Mode

Retracted: Partial Differential Equation Noise Reduction Model and Fuzzy Image Processing in Optimal Application of Sports Dance Exercise Training Mode

Fractional sampling operators of multivariate fuzzy functions and applications to image processing

In this work, we introduce a novel family of sampling-type operators of multivariate fuzzy-valued functions based on the fractional mean values of the approximated function. We discuss some convergence properties of these operators concerning an Lp-type fuzzy metric and examine the rate of convergence of the operators in Lipschitz classes of multivariate fuzzy valued functions. Some illustrative examples and graphs are given to demonstrate the convergence behavior of the operators in both one and two-dimensional cases. Finally, we present an algorithm for fuzzy image processing with numerical findings using the rule-based contrast enhancement method including a multidimensional fuzzy image matrix.

ℐ𝒢,𝒩-Implications Induced from Quasi-Grouping Functions and Negations on Bounded Lattices

Grouping functions, as one new case of not necessarily associative particular binary aggregation functions, have been proposed in the literature for their vast applications in fuzzy community detection problems, image processing and decision making. On the other hand, due to the wide applications in fuzzy reasoning, fuzzy control and approximate reasoning, the investigations of fuzzy implications derived from specific binary aggregation functions become a natural and hot research topic. In this paper, we focus on this research area and consider the [Formula: see text]-implications induced from quasi-grouping functions and negations on bounded lattices. To be precise, firstly, by removing the continuity condition, we extend the notion of grouping functions on the unit closed interval to the so-called quasi-grouping functions on bounded lattices. Secondly, we give some basic properties and two construction methods of quasi-grouping functions on bounded lattices. Finally, we give the concept of [Formula: see text]-implications and focus on the conditions under which they can satisfy the certain algebraic properties possessed by implications on bounded lattices.

Pseudo Overlap Functions, Fuzzy Implications and Pseudo Grouping Functions with Applications

Overlap and grouping functions are important aggregation operators, especially in information fusion, classification and decision-making problems. However, when we do more in-depth application research (for example, non-commutative fuzzy reasoning, complex multi-attribute decision making and image processing), we find overlap functions as well as grouping functions are required to be commutative (or symmetric), which limit their wide applications. For the above reasons, this paper expands the original notions of overlap functions and grouping functions, and the new concepts of pseudo overlap functions and pseudo grouping functions are proposed on the basis of removing the commutativity of the original functions. Some examples and construction methods of pseudo overlap functions and pseudo grouping functions are presented, and the residuated implication (co-implication) operators derived from them are investigated. Not only that, some applications of pseudo overlap (grouping) functions in multi-attribute (group) decision-making, fuzzy mathematical morphology and image processing are discussed. Experimental results show that, in many application fields, pseudo overlap functions and pseudo grouping functions have greater flexibility and practicability.

Defect Detection of Metro Wheel Set Tread Based on Image Recognition

In the operation of railway vehicles, the quality of bogies directly affects the operation quality and driving safety. Wheel set is one of the most important components in bogie, so the maintenance of wheel set is very important. For a long time, the detection of train wheel sets in China is still in the stage of manual measurement with backward technology and low efficiency. A new automatic detection method of wheel flange tread based on fuzzy neural network image processing technology is proposed in this paper. This method can accurately detect the defects of wheel flange tread. It collects the original image of the tested wheel set through the digital camera, inputs it into the computer, through certain calculation and processing, and compares it with the model established based on fuzzy neural network, so as to detect the defects of wheel flange and tread. First, the research status of wheel tread defect detection is summarized. Second, the basic principles of digital image technology are studied, the image processing models are confirmed, and the image processing method based on fuzzy neural network is established. Finally, eight wheel set treads are selected to carry out defect detection, and the analysis results show that the proposed method can obtain the better inspection precision.

Application of Blurred Image Processing and IoT Action Recognition in Sports Dance Sports Training.

In order to process the blurred image, this study proposes to combine the blurred point functions in the invariant space into multiple blurred images and then restore them through the deconvolution operation. The PSF functions of the fuzzy invariant space are combined to obtain the fuzzy invariant space. Finally, a gradual restoration method is used to perform many blurred image processing steps. The experimental results prove that the method proposed in this study can avoid the noise introduced in the process of multiple deconvolutions, can reduce the calculation error, and can improve the recovery effect. Based on fuzzy image processing, this research studies the nature of human motion and the identification of actions in the Internet of Things, which provides new ideas and methods for recognition research. The Kinect somatosensory camera of the Internet of Things is used to capture deep images, and 20 three-dimensional points of the human skeleton structure are obtained through its SDK. Based on this, the motion characteristics of human joints were studied, and a motion resolution model suitable for the Internet of Things was proposed. The model has low complexity, simple calculation, and sorting characteristics. Based on this, this research study also uses software engineering ideas and general methods of system development to design and create sports dance management information systems and uses advanced methods such as computers and the Internet to maintain training management to achieve optimal sports training for sports dance mode and to provide information about the management of sports dance athletes training to improve efficiency and the management level.

Partial Differential Equation Noise Reduction Model and Fuzzy Image Processing in Optimal Application of Sports Dance Exercise Training Mode

Ballroom dancing has been standardized in the United Kingdom since the beginning of the 20th century, and it has been competitive by WDC to form competitive ballroom dancing. Later, under the vigorous promotion of WDSF, it became a competitive sport recognized by the International Olympic Committee. In the process of teaching experiments, dancers’ wrong movements in sports dance training are inevitable. If they are not corrected in time, it will not only reduce the effect of dance expression, but also directly affect the improvement of sports performance. The dance images are often disturbed by many factors such as imaging equipment and the external environment during the process of acquisition, compression, transmission and storage, resulting in image distortion. Image distortion is mainly manifested in that the image structure information is submerged by noise, and the distorted image can no longer truly reflect the original image information, which brings difficulties to the post-processing of the image. In this paper, the image denoising method based on partial differential equation and its application in sports dance training are deeply studied.

Edge detection on fuzzy near sets

Fuzzy image processing involves various fuzzy approaches which help in understanding and processing of images as fuzzy sets. In the process of ED outlines, boundaries of an object and background in a image are detected. Hence edge detection is very important in digital image processing applications to obtain information from the frames as a forerunner step to feature extraction and object segmentation. In this paper the pixel values of an image is converted to fuzzy membership value matrix in which the columns are as fuzzy near sets and the corresponding membership and gray images are found using MATLAB. Using gray membership values some basic types of edge detection techniques are used and the corresponding fuzzy near membership matrices are constructed. The Euclidean and Hamming distances between two edge detection fuzzy value matrices are calculated and similarity measure is found. The higher value of similarity measure indicates that the images using two of the edge detection techniques are similar.

Fuzzy System Based Medical Image Processing for Brain Disease Prediction.

The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. A brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation) to ensure the model safety performance. Brain MRI images collected from a Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrate that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 s on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under DSC (Dice Similarity Coefficient), reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. In a word, the proposed algorithm can provide higher accuracy, a more apparent denoising effect, and the best segmentation and recognition effect than other models while ensuring energy consumption. The results can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.

Somatic cell count in buffalo milk using fuzzy clustering and image processing techniques.

This research communication presents an automatic method for the counting of somatic cells in buffalo milk, which includes the application of a fuzzy clustering method and image processing techniques (somatic cell count with fuzzy clustering and image processing|, SCCFCI). Somatic cell count (SCC) in milk is the main biomarker for assessing milk quality and it is traditionally performed by exhaustive methods consisting of the visual observation of cells in milk smears through a microscope, which generates uncertainties associated with human interpretation. Unlike other similar works, the proposed method applies the Fuzzy C-Means (FCM) method as a preprocessing step in order to separate the images (objects) of the cells into clusters according to the color intensity. This contributes signficantly to the performance of the subsequent processing steps (thresholding, segmentation and recognition/identification). Two methods of thresholding were evaluated and the Watershed Transform was used for the identification and separation of nearby cells. A detailed statistical analysis of the results showed that the SCCFCI method is able to provide results which are consistent with those obtained by conventional counting. This method therefore represents a viable alternative for quality control in buffalo milk production.

Estimating the Age of the Human by using Fuzzy Logic and Image Processing

Estimating the Age of the Human by using Fuzzy Logic and Image Processing

Analysis of Magnetic Resonance Imaging of Brain Tumour using Fuzzy Set Rules

Introduction: Cancer becomes life threatening once the expansion of tissues in human brain turns into an uncontrolled growth. In detection of brain tumours, Magnetic Resonance Imaging (MRI) images give better results when compared to Computerised Tomography (CT) scan and X-ray. Malignant tumours can be detected with the help of image processing and machine learning techniques. These techniques detect even a small abnormality in the human brain following a four-stage process which includes preprocessing, segmentation, feature extraction and optimisation. Aim: To predict a brain tumour using Fuzzy minimum-maximum rule in MRI. Materials and Methods: The medical challenge is how can we rapidly and precisely diagnose brain lesions. It is difficult when using standard image analysis to differentiate the benign and malignant lesions. Hereby, authors have used a Fuzzy imaging algorithm in data set of 253 brain tumour images of high grade tumour colllected from kaggle.com.This paper proposes a fuzzy min-max image processing algorithm. Image processing includes four stages- preprocessing, segmentation, feature extraction and accuracy detection. Brain tumours are located using various algorithms at each of these stages. Results: There were more than 20 features which can be taken into consideration when using the Fuzzy image algorithm. The proposed method in its current form achieves an accuracy of about 95% by considering seven features. Conclusion: The study revealed that age, shape, contour, blood supply, capsule of tumour, oedema, post contrast enhancement, cyst generation signal intensity of T-1 weighted image etc. were the important investigation parameters. Location and size are important to the domain experts, but due to their complexity these parameters are not considered here.

Intelligent Traffic Light Controller using Fuzzy Logic and Image Processing

Today's traffic congestion in the big city is a serious problem, as it causes a lot of environmental pollution and difficulty in transportation, which leads to difficult daily life for the human beings in addition to material losses. In this work a smart traffic light controller was designed using fuzzy logic and image processing with MATLAB, to control movement in two ways, aided by a camera and auto sensors. The Fuzzy logic has two inputs and six outputs designed, the console input is the number of cars on each road and the time of the assumed red, yellow and green signal according to the vehicles congestion. The simulation result is similar to the proposed control unit, as it deals with the lights simultaneously according to the number of cars in each branch of the road, which leads to the use of all the time to operate the stoplights. Our system can be employed in solving the problem of traffic congestion in the big cities or the smart cities.

A novel digital fuzzy system for image edge detection based on wrap-gate carbon nanotube transistors

This study presents a novel digital fuzzy approach for image edge detection based on carbon nanotube field effect transistor (CNTFET). The proposed design employs a simple fuzzy rule for digital image edge detection. To investigate the functionality and performance of the proposed fuzzy approach, our results are compared with the MATLAB edge detection operators such as Sobel, Canny, and Prewitt. Based on the simulation results, we demonstrate that the proposed fuzzy approach can be implemented through nanoscale logic gates and provide a reasonable accuracy. The core element of the proposed fuzzy system, implemented with CNTFETs, occupies 0.22 µm2 layout area and consumes 535 nW power consumption. Our results also emphasize the benefits of the Gate-All-Around (GAA) CNTFETs as a potential candidate for nanoscale digital fuzzy image processing applications.

Fuzzy Image Processing Based Architecture for Contrast Enhancement in Diabetic Retinopathy Images

Diabetic retinopathy” is damage to retina denotes one of the problems of diabetes which is a significant reason for visual infirmity and blindness. A comprehensive and routine eye check is important to early detection and rapid treatment. This study proposes a hardware system that can enhance the contrast in the diabetic retinopathy eye fundus images as a first step in different eye disease diagnoses. The fuzzy histogram equalization technique is proposed to increases the local contrast of Diabetic Retinopathy Images. First, a histogram construction hardware architecture for different image processing purposes has been built then modified with fuzzy techniques to create fuzzy histogram equalization architecture, which is used to enhance the original images. Both architectures are designed using a finite-state machine (FSM) technique and programmed with VHDL programming language. The first one is implemented using two (Spartan 3E-XC3S500 and Xilinx Artix-7 XC7A100T) kits, while the second architecture is implemented using (Spartan 3E-XC3S500) kit. The system consists also of a modified video graphics array (VGA) port to display the input and resulted images with a proper resolution. All the hardware outputs are compared to that results produce from MatLab for verification and the resulted images are tested by PSNR, MSE, ENTROPY ,and AMBE

Monitoring of Ocimum basilicum seeds growth with image processing and fuzzy logic techniques based on Cloudino-IoT and FIWARE platforms

The monitoring systems on the climatic conditions in the agriculture industry are increasingly useful and applicable since they allow to improve the processes of growth in the plants. This paper develops a monitoring system for the germination of Ocimum basilicum seeds using image processing and fuzzy logic techniques. Besides, the system is based on the Cloudino-IoT and FIWARE platforms as a real-time monitoring alternative. Therefore, temperature and humidity parameters can be observed in real-time. On the one hand, the system controls the variables through fuzzy control techniques and image processing monitors the germination in the radicle seed, and on the other, context data is used by the Cloudino-IoT and FIWARE platforms. On the other hand, the use of these combined open-source platforms allows cloud computing to be enabled to maintain, specify, analyze the data and automate the environmental parameters that allow maintaining temperature and humidity levels at optimal levels to create a favorable germination environment. The performance of the set-point control is compared with experimental results and the function interconnection of the IoT devices.