Cascaded and Hierarchical Neural Networks for Classifying Surface Images of Marble Slabs

Selver, M. Alper; Akay, Olcay; Ardali, E.; Yavuz, Ali; Önal, Okan; Özden, Gürkan

doi:10.1109/tsmcc.2009.2013816

Cited by 25 publications

(18 citation statements)

References 31 publications

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“…In this research, also correct classification rate (CCR) of K-means clustering using different two-and-three-level hierarchical clustering strategies (L: level) has been as following (Table 4). In research [26] examines classification of marble surfaces. In this research four image groups have been used (G1, G2, G3, G4) examined by different methods and tests.…”

Section: Resultsmentioning

confidence: 99%

“…Also implementing MLP classification in RGB colored space using SDH combination and wavelet for four image groups (G1, G2, G3, G4) with correct classification rate are respectively 97. 26 [32] marble classification has been examined using image processing techniques. The proposed method in this research includes two phases, training and classification.…”

Section: Resultsmentioning

confidence: 99%

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Marble Slabs Classification System Based on Image Processing (Ark Marble Mine in Birjand)

Moghaddam

Rajaei

Moghaddam³

2018

CivileJournal

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Marble is one of the semi-precious stones that has been used in decorating building façade and making decorative things. This stone is present in the nature in the form of rock or layered stone. Examining the kind of stone, extent of impurity and different streaks in white marble is a widely confronted subject by those who are involved in this industry. Obtaining the extent of impurity of white marble using methods of detecting and analyzing material is expensive and timeconsuming. In this research carried out on while marbles of Arc Mine in Birjand, it has been attempted to present very fast method using Image Processing Techniques so that while preserving identity and appearance of stone and without any damage to it, we compute the impurity level and different streaks on white marble surface. The proposed method includes two stages; in the first stage applying image processing functions, it is attempted to segment the present impurities and streaks on marble surface from the stone background and in the second stage, the area of these impurities and streaks is computed. Results obtained in this paper (97.8%) in comparison with other researches and experimental methods indicate acceptability of this algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Marble Slabs Classification System Based on Image Processing (Ark Marble Mine in Birjand)

Moghaddam

Rajaei

Moghaddam³

2018

CivileJournal

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“…NNs have been deployed in order to solve the classification problem of d ifferent automated systems [9][10][11][12][13][14][15]. Likewise NNs have been involved in the research of automated fabric defect inspection system.…”

Section: Literature Reviewmentioning

confidence: 99%

An Empirical Method for Optimization of Counterpropagation Neural Network Classifier Design for Fabric Defect Inspection

Habib¹,

Rokonuzzaman²

2014

IJISA

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Abstract-Automated, i.e. machine vision based fabric defect inspection systems have been drawing plenty of attention of the researchers in order to replace manual inspection. Two difficult problems are mainly posed by automated fabric defect inspection systems. They are defect detection and defect classification. Counterpropagation neural network (CPN) is a robust classifier and very promising for defect classification. In general, works reported to date have claimed varying level of successes in detection and classification of different types of defects through CPN; but in particular, no claimed has been made for successful application of CPN for fabric defects detection and classification. In those published works, no investigation has been reported regarding to the variation of major performance parameters of NN based classifiers such as learning time and classification accuracy based on network topology and training parameters. As a result, application engineer has little or no guidance to take design decisions for reaching to optimum structure of NN based defect classifiers in general and CPN based in particular. Our work focuses on empirical investigation of interrelationship between design parameters and performance of CPN based classifier for fabric defect classification. It is believed that such work will be laying the ground to empower application engineers to decide about optimum values of design parameters for realizing most appropriate CPN based classifier.

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“…Due to the wet and dirty working conditions and to the nonhomogeneous and nonconstant environmental physical properties, also other contactless sensors proposed or potentially suitable for marble machines, based on LASER or vision systems or ultrasonic waves [5][6][7][8], turned out not to be suitable for successful industrial applications.…”

Section: Process Control and Sensors Inmentioning

confidence: 99%

“…Capacitive sensors for marble [9] and more complex ultrasound-or georadar-based systems [6,8,10] have been studied in the literature. However, their target is the �ne-grain analysis of the porosity and defects of stone materials (e.g., measuring the dielectric permittivity variations) in a controlled working environment (dry, clean, and with still stone samples) rather than the real-time detection of the presence of a marble slab during the working process inside an industrial machine.…”

Section: Process Control and Sensors Inmentioning

confidence: 99%

Wireless Sensing Based on RFID and Capacitive Technologies for Safety in Marble Industry Process Control

Iacopetti

Saponara

Fanucci

et al. 2013

Journal of Computer Networks and Communications

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This paper presents wireless sensing systems to increase safety and robustness in industrial process control, particularly in industrial machines for marble slab working. The process is performed by abrasive or cutting heads activated independently by the machine controller when the slab, transported on a conveyer belt, is under them. Current slab detection systems are based on electromechanical or optical devices at the machine entrance stage, suffering from deterioration and from the harsh environment. Slab displacement or break inside the machine due to the working stress may result in safety issues and damages to the conveyer belt due to incorrect driving of the working tools. The experimented contactless sensing techniques are based on four RFID and two capacitive sensing technologies and on customized hardware/software. The proposed solutions aim at overcoming some limitations of current state-of-the-art detection systems, allowing for reliable slab detection, outside and/or inside the machine, while maintaining low complexity and at the same time robustness to industrial harsh conditions. The proposed sensing devices may implement a wireless or wired sensor network feeding detection data to the machine controller. Data integrity check and process control algorithms have to be implemented for the safety and reliability of the overall industrial process.

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Cascaded and Hierarchical Neural Networks for Classifying Surface Images of Marble Slabs

Cited by 25 publications

References 31 publications

Marble Slabs Classification System Based on Image Processing (Ark Marble Mine in Birjand)

Marble Slabs Classification System Based on Image Processing (Ark Marble Mine in Birjand)

An Empirical Method for Optimization of Counterpropagation Neural Network Classifier Design for Fabric Defect Inspection

Wireless Sensing Based on RFID and Capacitive Technologies for Safety in Marble Industry Process Control

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