scholarly journals Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7396
Author(s):  
Bubryur Kim ◽  
Se-Woon Choi ◽  
Gang Hu ◽  
Dong-Eun Lee ◽  
Ronnie O. Serfa Serfa Juan

With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure’s existing infrastructure by recording and analyzing exterior damages. Therefore, it is desirable to have an automated approach that reports defects on images reliably and robustly. This paper presents a multivariate analysis approach for images, specifically for assessing substantial damage (such as cracks). The image analysis provides graph representations that are related to the image, such as the histogram. In addition, image-processing techniques such as grayscale are also implemented, which enhance the object’s information present in the image. In addition, this study uses image segmentation and a neural network, for transforming an image to analyze it more easily and as a classifier, respectively. Initially, each concrete structure image is preprocessed to highlight the crack. A neural network is used to calculate and categorize the visual characteristics of each region, and it shows an accuracy for classification of 98%. Experimental results show that thermal image extraction yields better histogram and cumulative distribution function features. The system can promote the development of various thermal image applications, such as nonphysical visual recognition and fault detection analysis.

2021 ◽  
Vol 11 (18) ◽  
pp. 8388
Author(s):  
Bubryur Kim ◽  
Ronnie O. Serfa Juan ◽  
Dong-Eun Lee ◽  
Zengshun Chen

Infrared thermography is the science of measuring the infrared energy emitted by an object, translating it to apparent temperature variance, and displaying the result as an infrared image. Significantly, acquiring thermal images delivers distinctive levels of temperature differences in solar panels that correspond to their health status, which is beneficial for the early detection of defects. The proposed algorithm aims to analyze the thermal solar panel images. The acquired thermal solar panel images were segmented into solar cell sizes to provide more detailed information by region or cell area instead of the entire solar panel. This paper uses both the image histogram information and its corresponding cumulative distribution function (CDF), useful for image analysis. The acquired thermal solar panel images are enhanced using grayscale, histogram equalization, and adaptive histogram equalization to represent a domain that is easier to analyze. The experimental results reveal that the extraction results of thermal images provide better histogram and CDF features. Furthermore, the proposed scheme includes the convolutional neural network (CNN) for classifying the enhanced images, which shows that a 97% accuracy of classification was achieved. The proposed scheme could promote different thermal image applications—for example, non-physical visual recognition and fault detection analysis.


2014 ◽  
Vol 37 (3) ◽  
pp. 257-263 ◽  
Author(s):  
Poonpat Poonnoy ◽  
Panupong Yodkeaw ◽  
Akkarin Sriwai ◽  
Pongpol Umongkol ◽  
Saowanit Intamoon

Author(s):  
Thi Thoa Mac ◽  
Nguyen Thanh Hung

The pill manufacturing process accrues substantial financial costs due to quality. Pill quality inspection is laborious, time-consuming and subjective, resulting in poor statistical representation and inconsistent results. In this study, we developed an approach that integrates deep learning algorithms and computer-vision-based processing with an optimization algorithm to fully automate the image analysis of internal crack/contamination detection. This approach exploits the features learned by convolutional neural network using various sub-processing techniques and Adam optimization. It achieves robust quantification of internal pill defects with an average accuracy of 95%.


2021 ◽  
Vol 13 (2) ◽  
pp. 12-24
Author(s):  
Rafael Yuji Hirata Furusho ◽  
Francisco Assis da Silva ◽  
Leandro Luiz de Almeida ◽  
Danillo Roberto Pereira ◽  
Mário Augusto Pazoti ◽  
...  

Unlike most Western countries, which have a Latin-derived base alphabet, Japan has two syllabic alphabets called Hiragana and Katakana, and a Chinese alphabet, called Kanji. The vast differences in the writing of these Eastern alphabets to Western alphabets, Western alphabet-based OCR algorithms tend not to efficiently detect Japanese characters. This work contributes to a methodology applying digital image processing techniques, such as color range-based segmentation, edge detection and mathematical morphology techniques, to detect Japanese traffic informationalplates correctly the perspective and segment the characters contained in it. A convolutional neural network wasused to perform the classification of Hiragana characters contained in the segmented plates, withaccuracyof 94.37%.


Author(s):  
Abraham Pouliakis ◽  
Vasileia Damaskou ◽  
Niki Margari ◽  
Efrossyni Karakitsou ◽  
Vasilios Pergialiotis ◽  
...  

The aim of this study is to compare machine learning algorithms (MLAs) in the discrimination between benign and malignant endometrial nuclei and lesions. Nuclei characteristics are obtained via image analysis and were measured from liquid-based cytology slides. Four hundred sixteen histologically confirmed patients were involved, 168 healthy, and the remaining with pathological endometrium. Fifty percent of the cases were used to three MLAs: a feedforward artificial neural network (ANN) trained by the backpropagation algorithm, a learning vector quantization (LVQ), and a competitive learning ANN. The outcome of this process was the classification of cell nuclei as benign or malignant. Based on the nuclei classification, an algorithm to classify individual patients was constructed. The sensitivity of the MLAs in training set for nuclei classification was in the range of 77%-84%. Patients' classification had sensitivity in the range of 90%-98%. These findings indicate that MLAs have good performance for the classification of endometrial nuclei and lesions.


2020 ◽  
pp. 266-279
Author(s):  
Abraham Pouliakis ◽  
Niki Margari ◽  
Effrosyni Karakitsou ◽  
Evangelia Alamanou ◽  
Nikolaos Koureas ◽  
...  

Aim of this article is to investigate the potential of Artificial Intelligence (AI) in the discrimination between benign and malignant endometrial nuclei and lesions. For this purpose, 416 histologically confirmed liquid-based cytological smears were collected and morphometric characteristics of cell nuclei were measured via image analysis. Then, 50% of the cases were used to train an AI system, specifically a learning vector quantization (LVQ) neural network. As a result, cell nuclei were classified as benign or malignant. Data from the remaining 50% of the cases were used to evaluate the AI system performance. By nucleic classification, an algorithm for the classification of individual patients was constructed, and performance indices on patient classification were calculated. The sensitivity for the classification of nuclei was 77.95%, and the specificity was 73.93%. For the classification of individual patients, the sensitivity was 90.70% and the specificity 82.79%. These results indicate that an AI system can have an important role in endometrial lesions classification.


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