scholarly journals Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Van-Duc Tran
Keyword(s):  
Image Processing ◽  
Image Texture ◽  
Rank Test ◽  
Support Vector ◽  
Gray Level ◽  
Pipe Surface ◽  
Pipe System ◽  
Building Maintenance ◽  
Promising Tool ◽  
Occurrence Matrix

To maintain the serviceability of buildings, the owners need to be informed about the current condition of the water supply and waste disposal systems. Therefore, timely and accurate detection of corrosion on pipe surface is a crucial task. The conventional manual surveying process performed by human inspectors is notoriously time consuming and labor intensive. Hence, this study proposes an image processing-based method for automating the task of pipe corrosion detection. Image texture including statistical measurement of image colors, gray-level co-occurrence matrix, and gray-level run length is employed to extract features of pipe surface. Support vector machine optimized by differential flower pollination is then used to construct a decision boundary that can recognize corroded and intact pipe surfaces. A dataset consisting of 2000 image samples has been collected and utilized to train and test the proposed hybrid model. Experimental results supported by the Wilcoxon signed-rank test confirm that the proposed method is highly suitable for the task of interest with an accuracy rate of 92.81%. Thus, the model proposed in this study can be a promising tool to assist building maintenance agents during the phase of pipe system survey.

scholarly journals Perbandingan Metode Klasifikasi pada Pengolahan Citra Mata Ikan Tuna

2020 ◽  
Vol 5 ◽  
Author(s):  
Toni Dwi Novianto ◽  
I Made Susi Erawan
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Region Of Interest ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Gray Level ◽  
K Nearest Neighbors ◽  
Eye Color ◽  
Occurrence Matrix ◽  
Fish Eye

<p class="AbstractEnglish"><strong>Abstract:</strong> Fish eye color is an important attribute of fish quality. The change in eye color during the storage process correlates with freshness and has a direct effect on consumer perception. The process of changing the color of the fish eye can be analyzed using image processing. The purpose of this study was to obtain the best classification method for predicting fish freshness based on image processing in fish eyes. Three tuna fish were used in this study. The test was carried out for 20 hours with an eye image every 2 hours at room temperature. Fish eye image processing uses Matlab R.2017a software while the classification uses Weka 3.8 software. The image processing stages are taking fish eye image, segmenting ROI (region of interest), converting RGB image to grayscale, and feature extraction. Feature extraction used is the gray-level co-occurrence matrix (GLCM). The classification techniques used are artificial neural networks (ANN), k-neighborhood neighbors (k-NN), and support vector machines (SVM). The results showed the value using ANN = 0.53, k-NN = 0.83, and SVM = 0.69. Based on these results it can be determined that the best classification technique is to use the k-nearest neighbor (k-NN).</p><p class="AbstrakIndonesia"><strong>Abstrak:</strong> Warna mata ikan merupakan atribut penting pada kualitas ikan. Perubahan warna mata ikan selama proses penyimpanan berhubungan dengan tingkat kesegaran dan memiliki efek langsung pada persepsi konsumen. Proses perubahan warna mata ikan dapat dianalisis menggunakan pengolahan citra. Tujuan penelitian ini adalah mendapatkan metode klasifikasi terbaik untuk memprediksi kesegaran ikan berbasis pengolahan citra pada mata ikan. Tiga ekor ikan tuna digunakan dalam penelitian ini. Pengujian dilakukan selama 20 jam dengan pengambilan citra mata setiap 2 jam pada suhu ruang. Pengolahan citra mata ikan menggunakan software matlab R.2017a sedangkan pengklasifiannya menggunakan software Weka 3.8. Tahapan pengolahan citra meliputi pengambilan citra mata ikan, segmentasi ROI (<em>region of interest</em>), konversi citra RGB menjadi <em>grayscale</em>, dan ekstraksi fitur. Ekstraksi fitur yang digunakan yaitu <em>gray-level co-occurrence matrix</em> (GLCM).  Teknik klasifikasi yang digunakan yaitu, <em>artificial neural network</em> (ANN), <em>k-nearest neighbors</em> (k-NN), dan <em>support vector machine</em> (SVM). Hasil penelitian menunjukkan nilai korelasi menggunakan ANN = 0,53, k-NN = 0,83, dan SVM = 0,69. Berdasarkan hasil tersebut dapat disimpulkan teknik klasifikasi terbaik adalah menggunakan <em>k-nearest neighbors</em> (k-NN).</p>

scholarly journals Digit Classification of Majapahit Relic Inscription using GLCM-SVM

2018 ◽  
Vol 1 (2) ◽  
pp. 46
Author(s):  
Tri Septianto ◽  
Endang Setyati ◽  
Joan Santoso
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Template Matching ◽  
Support Vector ◽  
Gray Level ◽  
Class 1 ◽  
Occurrence Matrix ◽  
Handwritten Recognition

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

scholarly journals Image Processing-Based Pitting Corrosion Detection Using Metaheuristic Optimized Multilevel Image Thresholding and Machine-Learning Approaches

2020 ◽  
Vol 2020 ◽  
pp. 1-19 ◽  
Author(s):  
Nhat-Duc Hoang
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Pitting Corrosion ◽  
Image Texture ◽  
Support Vector ◽  
Learning Approaches ◽  
Image Texture Analysis ◽  
Image Thresholding ◽  
Structural Health ◽  
Promising Tool

Pitting corrosion can lead to critical failures of infrastructure elements. Therefore, accurate detection of corroded areas is crucial during the phase of structural health monitoring. This study aims at developing a computer vision and data-driven method for automatic detection of pitting corrosion. The proposed method is an integration of the history-based adaptive differential evolution with linear population size reduction (LSHADE), image processing techniques, and the support vector machine (SVM). The implementation of the LSHADE metaheuristic in this research is multifold. This optimization algorithm is employed in the task of multilevel image thresholding to extract regions of interest from the metal surface. Image texture analysis methods of statistical measurements of color channels, gray-level co-occurrence matrix, and local binary pattern are used to compute numerical features subsequently employed by the SVM-based pattern recognition phase. In addition, the LSHADE metaheuristic is also used to optimize the hyperparameters of the machine-learning approach. Experimental results supported by statistical test points out that the newly developed approach can attain a good predictive result with classification accurate rate = 91.80%, precision = 0.91, recall = 0.94, negative predictive value = 0.93, and F1 score = 0.92. Thus, the newly developed method can be a promising tool to be used in a periodic structural health survey.

Recaptured Image Forensics Algorithm Based on Image Texture Feature

2019 ◽  
Vol 34 (03) ◽  
pp. 2054011
Author(s):  
Yanjun Sun ◽  
Xuanjing Shen ◽  
Changming Liu ◽  
Yongzhe Zhao
Keyword(s):  
Digital Image ◽  
Rapid Development ◽  
Image Forensics ◽  
Detection Algorithm ◽  
Digital Image Forensics ◽  
Image Texture ◽  
Support Vector ◽  
Gray Level ◽  
Identification Rate ◽  
Occurrence Matrix

With the rapid development of digital phones, the digital image forensics system in current times has had a great impact. It will lead to a serious threat for us, and especially the emergence of the recaptured image makes the existing digital image forensics algorithm invalid. So, it needs an effective image detection algorithm for us to identify recaptured images. In this paper, a new detection algorithm of the recaptured image is presented based on gray level co-occurrence matrix by analyzing the differences between the real and recaptured images. In order to analyze the differences, a new image evaluation model was put forward in this paper, which is called image variance ratio. Firstly, the algorithm proposed extracted high-frequency and low-frequency information of images by wavelet transform, based on which we calculated the relative gray level co-occurrence matrices. Secondly, the features of gray level co-occurrence matrix were extracted. At last, the recaptured image was classified by the support vector machine according to the features. The experimental results showed the algorithm proposed can not only effectively identify the recaptured image obtained from different media but also have better identification rate.

scholarly journals Modified KNN-LVQ for Stairs Down Detection Based on Digital Image

2021 ◽  
Vol 12 (3) ◽  
pp. 141
Author(s):  
Ahmad Wali Satria Bahari Johan ◽  
Sekar Widyasari Putri ◽  
Granita Hajar ◽  
Ardian Yusuf Wicaksono
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Hybrid Method ◽  
Visually Impaired ◽  
Blind People ◽  
Gray Level ◽  
Computational Speed ◽  
Occurrence Matrix ◽  
Computing Speed

Persons with visual impairments need a tool that can detect obstacles around them. The obstacles that exist can endanger their activities. The obstacle that is quite dangerous for the visually impaired is the stairs down. The stairs down can cause accidents for blind people if they are not aware of their existence. Therefore we need a system that can identify the presence of stairs down. This study uses digital image processing technology in recognizing the stairs down. Digital images are used as input objects which will be extracted using the Gray Level Co-occurrence Matrix method and then classified using the KNN-LVQ hybrid method. The proposed algorithm is tested to determine the accuracy and computational speed obtained. Hybrid KNN-LVQ gets an accuracy of 95%. While the average computing speed obtained is 0.07248 (s).

An enhanced method for the identification of ferritic morphologies in welded fusion zones based on gray-level co-occurrence matrix: A computational intelligence approach

2020 ◽  
pp. 095440622094226 ◽  
Author(s):  
Amanda Campos Souza ◽  
Gulliver Catão Silva ◽  
Lecino Caldeira ◽  
Fernando Marques de Almeida Nogueira ◽  
Moisés Luiz Lagares Junior ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Intelligence ◽  
Low Carbon Steel ◽  
Support Vector ◽  
Gray Level ◽  
Low Carbon ◽  
Identification Rate ◽  
Artificial Neural ◽  
Occurrence Matrix

This work focuses on the identification of five of the most common ferritic morphologies present in welded fusion zones of low carbon steel through images acquired by photomicrographies. With this regards, we discuss the importance of the gray-level co-occurrence matrix to extract the features to be used as the input of the computational intelligence techniques. We use artificial neural networks and support vector machines to identify the proportions of each morphology and present the error identification rate for each technique. The results show that the use of gray-level co-occurrence extraction allows a less intense computational model with statistical validity and the support vector machine as a computational intelligence technique allows smaller variability when compared to the artificial neural networks.

scholarly journals Classification for Liver Diseases Based on Ultrasound Image Texture Features

2018 ◽  
Author(s):  
Sendren Sheng-Dong Xu ◽  
Chien-Tien Su ◽  
Chun-Chao Chang ◽  
Pham Quoc Phu
Keyword(s):  
Liver Abscess ◽  
Liver Diseases ◽  
Ultrasound Image ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Gray Level ◽  
Forward Selection

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

scholarly journals Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix

2011 ◽  
Vol 10 (3) ◽  
pp. 73-79 ◽  
Author(s):  
Jian Yang ◽  
Jingfeng Guo
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Texture Feature ◽  
Local Area ◽  
Image Texture ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Feature Extraction Method ◽  
Level Difference ◽  
Occurrence Matrix

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

scholarly journals LBP and GLCM Based Image Forgery Recognition

2018 ◽  
Vol 7 (4.6) ◽  
pp. 217
Author(s):  
D. Vaishnavi ◽  
T. S. Subashini ◽  
G. N. Balaji ◽  
D. Mahalakshmi
Keyword(s):  
Neural Network ◽  
Local Binary Pattern ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Gray Level ◽  
Image Splicing ◽  
Naked Eye ◽  
Occurrence Matrix

The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged images among the authentic images is done using the popular pattern recognition algorithms such as combined k-NN (Comb-KNN), back propagation neural network (BPNN) and support vector machine (SVM). The recorded results are also compared with the existing results of the previous studies to ascertain the quality of the results.  

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