scholarly journals The Application of Computer Image Analysis Based on Textural Features for the Identification of Barley Kernels Infected with Fungi of the Genus Fusarium

2018 ◽  
Vol 22 (3) ◽  
pp. 49-56 ◽  
Author(s):  
Ewa Ropelewska
Keyword(s):  
Image Analysis ◽  
Selection Method ◽  
Color Model ◽  
Textural Features ◽  
Discriminative Power ◽  
Selection Methods ◽  
Computer Image Analysis ◽  
Genus Fusarium ◽  
Color Models ◽  
Lab Color Model

AbstractThe aim of this study was to develop discrimination models based on textural features for the identification of barley kernels infected with fungi of the genus Fusarium and healthy kernels. Infected barley kernels with altered shape and discoloration and healthy barley kernels were scanned. Textures were computed using MaZda software. The kernels were classified as infected and healthy with the use of the WEKA application. In the case of RGB, Lab and XYZ color models, the classification accuracies based on 10 selected textures with the highest discriminative power ranged from 95 to 100%. The lowest result (95%) was noted in XYZ color model and Multi Class Classifier for the textures selected using the Ranker method and the OneR attribute evaluator. Selected classifiers were characterized by 100% accuracy in the case of all color models and selection methods. The highest number of 100% results was obtained for the Lab color model with Naive Bayes, LDA, IBk, Multi Class Classifier and J48 classifiers in the Best First selection method with the CFS subset evaluator.

scholarly journals Silhouette index for determining optimal k-means clustering on images in different color models

2018 ◽  
Vol 7 (2.14) ◽  
pp. 105 ◽  
Author(s):  
Abd Rasid Mamat ◽  
Fatma Susilawati Mohamed ◽  
Mohamad Afendee Mohamed ◽  
Norkhairani Mohd Rawi ◽  
Mohd Isa Awang
Keyword(s):  
Image Processing ◽  
Color Model ◽  
Cluster Validation ◽  
Validation Process ◽  
Color Models ◽  
Silhouette Index ◽  
Hsv Color Model ◽  
Different Color ◽  
Lab Color Model ◽  
Cie Lab

Clustering process is an essential part of the image processing. Its aim to group the data according to having the same attributes or similarities of the images. Consequently, determining the number of the optimum clusters or the best (well-clustered) for the image in different color models is very crucial. This is because the cluster validation is fundamental in the process of clustering and it reflects the split between clusters. In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters. Next, the Silhouette Index (SI) is used to the cluster validation process, and this value is range between 0 to 1 and the greater value of SI illustrates the best of cluster separation. The results from several experiments show that the best cluster separation occurs when k=2 and the value of average SI is inversely proportional to the number of k cluster for all color model. The result shows in HSV color model the average SI decreased 14.11% from k = 2 to k = 8, 11.1% in HSV color model and 16.7% in CIE Lab color model. Comparisons are also made for the three color models and generally the best cluster separation is found within HSV, followed by the RGB and CIE Lab color models.  

scholarly journals Using the pixel information value parameter in an entropy image analysis criterion

2018 ◽  
pp. 67-72
Author(s):  
R. Kvyetnyy ◽  
A. Olesenko
Keyword(s):  
Image Analysis ◽  
Standard Test ◽  
Information Value ◽  
Color Model ◽  
Adjacent Pixel ◽  
Lab Color Model ◽  
Entropy Criterion ◽  
Modified Entropy

The work is devoted to the development and research of the entropy criterion of image analysis on its corresponding to the method of RLE-compression. The Lab color model and the CIEDE1976 color estimation metric have been analyzed. The pixel information importance parameter has been introduced, which is based on the above described metric and allows us to estimate the importance of the adjacent pixel taking into consideration the information it introduces in relation to the previous pixel. The modified entropy image analysis criterion has been developed taking into account the pixel’s information importance parameter. The adequacy of the proposed criterion has been checked on the sample of standard test images and the feasibility of its use has been proved.

Recent developments in automated Transmission Electron MI

1989 ◽  
Vol 47 ◽  
pp. 46-47
Author(s):  
W.J. de Ruijter ◽  
P. Rez ◽  
David J. Smith
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
Lens Design ◽  
Computer Image Analysis ◽  
Transmission Electron ◽  
Resolution Electron ◽  
Recent Developments ◽  
On Line ◽  
Routine Procedures ◽  
Near Future

There is growing interest in the on-line use of computers in high-resolution electron n which should reduce the demands on highly skilled operators and thereby extend the r of the technique. An on-line computer could obviously perform routine procedures hand, or else facilitate automation of various restoration, reconstruction and enhan These techniques are slow and cumbersome at present because of the need for cai micrographs and off-line processing. In low resolution microscopy (most biologic; primary incentive for automation and computer image analysis is to create a instrument, with standard programmed procedures. In HREM (materials researc computer image analysis should lead to better utilization of the microscope. Instru (improved lens design and higher accelerating voltages) have improved the interpretab the level of atomic dimensions (approximately 1.6 Å) and instrumental resolutior should become feasible in the near future.

The Effectiveness of the Fused Weighted Filter Feature Selection Method to Improve Software Fault Prediction

2016 ◽  
Vol 8 ◽  
pp. 5 ◽  
Author(s):  
Fatemeh Alighardashi ◽  
Mohammad Ali Zare Chahooki
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Machine Learning Algorithms ◽  
Fault Prediction ◽  
Filter Method ◽  
Selection Methods ◽  
Software Projects ◽  
Software Fault Prediction ◽  
Software Fault

Improving the software product quality before releasing by periodic tests is one of the most expensive activities in software projects. Due to limited resources to modules test in software projects, it is important to identify fault-prone modules and use the test sources for fault prediction in these modules. Software fault predictors based on machine learning algorithms, are effective tools for identifying fault-prone modules. Extensive studies are being done in this field to find the connection between features of software modules, and their fault-prone. Some of features in predictive algorithms are ineffective and reduce the accuracy of prediction process. So, feature selection methods to increase performance of prediction models in fault-prone modules are widely used. In this study, we proposed a feature selection method for effective selection of features, by using combination of filter feature selection methods. In the proposed filter method, the combination of several filter feature selection methods presented as fused weighed filter method. Then, the proposed method caused convergence rate of feature selection as well as the accuracy improvement. The obtained results on NASA and PROMISE with ten datasets, indicates the effectiveness of proposed method in improvement of accuracy and convergence of software fault prediction.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

Computer image analysis of intramuscular adipocytes and marbling in the longissimus muscle of cattle1

2006 ◽  
Vol 84 (12) ◽  
pp. 3251-3258 ◽  
Author(s):  
X. J. Yang ◽  
E. Albrecht ◽  
K. Ender ◽  
R. Q. Zhao ◽  
J. Wegner
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
Longissimus Muscle ◽  
Computer Image Analysis

scholarly journals Computer Image Analysis for Marbling Change within Longissimus Muscle with Cut Section of Japanese Black Steers

2007 ◽  
Vol 78 (4) ◽  
pp. 441-446 ◽  
Author(s):  
Yoshinobu NAKAHASHI ◽  
Shin MARUYAMA ◽  
Shinji SEKI ◽  
Satoshi HIDAKA ◽  
Keigo KUCHIDA
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
Longissimus Muscle ◽  
Computer Image Analysis

Digital image analysis in retinopathy of prematurity: A comparison of vessel selection methods

2012 ◽  
Vol 16 (3) ◽  
pp. 223-228 ◽  
Author(s):  
Clare M. Wilson ◽  
Karen Wong ◽  
Jeffery Ng ◽  
Kenneth D. Cocker ◽  
Anna L. Ells ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Retinopathy Of Prematurity ◽  
Digital Image Analysis ◽  
Selection Methods

scholarly journals Using systematic sampling selection for Monte Carlo solutions of Feynman-Kac equations

2008 ◽  
Vol 40 (2) ◽  
pp. 454-472 ◽  
Author(s):  
Ivan Gentil ◽  
Bruno Rémillard
Keyword(s):  
Monte Carlo ◽  
Markov Chains ◽  
Selection Method ◽  
Systematic Sampling ◽  
Selection Methods ◽  
Convergence Properties ◽  
Main Motivation ◽  
Empirical Basis ◽  
Convergence Results ◽  
Selection For

While the convergence properties of many sampling selection methods can be proven, there is one particular sampling selection method introduced in Baker (1987), closely related to ‘systematic sampling’ in statistics, that has been exclusively treated on an empirical basis. The main motivation of the paper is to start to study formally its convergence properties, since in practice it is by far the fastest selection method available. We will show that convergence results for the systematic sampling selection method are related to properties of peculiar Markov chains.

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