Plant disease leaf image segmentation plays an important role in the plant disease detection through leaf symptoms. A novel segmentation method of plant disease leaf image is proposed based on a hybrid clustering. The whole color leaf image is firstly divided into a number of compact and nearly uniform superpixels by superpixel clustering, which can provide useful clustering cues to guide image segmentation to accelerate the convergence speed of the expectation maximization (EM) algorithm, and then, the lesion pixels are quickly and accurately segmented from each superpixel by EM algorithm. The experimental results and the comparison results with similar approaches demonstrate that the proposed method is effective and has high practical value for plant disease detection.

Plant disease leaf image segmentation based on superpixel clustering and EM algorithm

Fusion of superpixel, expectation maximization and PHOG for recognizing cucumber diseases

Water pressure management in a water distribution network (WDN) is a key component applied to achieve desirable water quality as well as a trouble-free operation of the network. This paper presents a hybrid, two-stage approach, to provide optimal separation of a WDN into District Metered Areas (DMAs), improving both water age and pressure. The first stage aims to divide the WDN into smaller areas via the Geometric Partitioning method, which is based on Recursive Coordinate Bisection (RCB). Subsequently, the Student’s t-mixture model (SMM) is applied to each area, providing an optimal placement of isolation valves and separating the network in DMAs. The model is evaluated on a realistic network generated through Watergems and is compared against one variation of it implemented, including the Gaussian Mixture Model (GMM) as well as the Genetic Algorithm (GA) approach, obtaining impressive performance. The implementation of both stages was deployed in a MATLAB environment through the Epanet toolkit. The proposed system is very promising, especially for large size WDNs due to the decreased running time and noteworthy reduction of pressure and water age.

https://www.mdpi.com/2073-4441/11/2/278/pdf

Optimizing the Formation of DMAs in a Water Distribution Network through Advanced Modelling

Components made of polymeric composite materials, such as wings and stabilizers of aircrafts, are periodically inspected using non-destructive testing methods. Ultrasonic testing is one of the primary inspection methods applied in the aircraft/aerospace industry. Image processing methods have been developed for the purpose of ultrasonic data analysis and increasing the inspection efficiency. A critically important factor in damage sizing is appropriate processing of ultrasonic data in order to extract the damage region properly before calculation of its extent. The paper presents a comparative analysis of various image segmentation methods in the light of accuracy of damage detection in ultrasonic C-Scans of composite structures. A brief review of image segmentation methods is presented and their usefulness in the ultrasonic testing applications is discussed. The selected methods, namely the threshold-, edge-, region-, and clustering-based ones, were tested using ultrasonic C-Scans of a specimen with barely visible impact damage and aircraft panels with delamination, all made of CFRP composites. A problem with the selection of appropriate input parameters using most of the segmentation methods is discussed, and non-parametric histogram-based approaches are proposed. A quantitative analysis of the accuracy of damage detection using the segmentation methods is presented and the most suitable approaches are introduced. The proposed processing procedures may significantly improve the objectivity of inspections of composite elements and structures using ultrasonic testing.

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Uncertainty Estimation for Ultrasonic Inspection of Composite Aerial Structures

Image segmentation is the method of partitioning an image into some homogenous regions that are more meaningful for its better understanding and examination. Soft computing methods having the capabilities of achieving artificial intelligence are predominately used to perform the task of segmentation. Due to the variability and the uncertainty present in natural scenes, segmentation is a complicated task to perform with the help of conventional image segmentation techniques. Therefore, in this article a hybrid Fuzzy Competitive Learning based Counter Propagation Network (FCPN) is proposed for the segmentation of natural scene images. This method compromises of the uncertainty handling capabilities of the fuzzy system and proficiency of parallel learning ability of neural network. To identify the number of clusters automatically in less computational time, the instar layer of Counter propagation network (CPN) has been trained by using Fuzzy competitive learning (FCL). The outstar layer of counter propagation network is trained by using Grossberg learning for obtaining the desired output. Region growing method having the tendency to correctly identify edges with simplicity is used for initial seed point selection. Then, the most similar regions in the image are clustered and the number of clusters is estimated automatically. Finally, by identifying the cluster centers the images are segmented. Bacterial foraging algorithm is used to initialize the initial weights to the network, which helps the proposed method in achieving low convergence ratio with higher accuracy. Results validated the higher performance of proposed FCPN method when compared with other states-of-the-art methods. For future work, some other adaptive methods like the fuzzy model-based network can be used to identify multiple object regions and classifying them among separate clusters.

Image segmentation using fuzzy competitive learning based counter propagation network

The segmentation of color image is an important research field of image processing and pattern recognition A color image could be considered as the result from Gaussian mixture model (GMM) to which several Gaussian random variables contribute In this paper, an efficient method of image segmentation is proposed The method uses Gaussian mixture models to model the original image, and transforms segmentation problem into the maximum likelihood parameter estimation by expectation-maximization (EM) algorithm And using the method to classify their pixels of the image, the problem of color image segmentation can be resolved to some extent The experiment results confirm this method validity

Zhaoxia Fu

Papers

Color Image Segmentation Using Gaussian Mixture Model and EM Algorithm