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

Moghaddam, Hossein Kardan; Rajaei, Amir; Moghaddam, Hamid Kardan

doi:10.28991/cej-030972

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…Moghaddam et al 2018 in [18] proposed a two-step method for white marble classification. The first step uses image processing to segments impurities on marble surface, while in second step area of impurities is computed.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating Artificial Intelligence and Modern Technologies Enhancement in Stone and Marble Cutting in Palestine

2024

jjmie

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Stone and Marble sector in Palestine has a significant contribution to the GDP. There are some modern factories that use computerized and numerical controls for stone cutting employing modern tools and processes, but most of the marble cutting factories depend on manual and small machinery production lines. This paper examines integrating modern technologies; artificial intelligence (AI) and mechatronic systems like sensors, actuators and control systems, in these cutting factories in order to increase their efficiency, improve the added value and contribute in the occupational safety for human labor and cutting processes. In particular artificial intelligence, machine learning and image processing tools will be investigated as examples of modern technologies that can be implemented in marble and stone cutting processes in Palestine. Four Convolutional Natural Network (CNN) types were employed to classify marble slabs, comparing colored and gray-level databases. Gray-level yielded superior recognition rates due to marble's prevalent gray color. Texture, not color, drove classification; ResNet-152 achieved 100% Recognition Rate (RR) for gray-level and 98.6% for colored. In terms of efficiency, Inception CNN excelled. Ultimately, gray-level images best served marble classification, rendering color irrelevant.

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Section: Literature Reviewmentioning

confidence: 99%

Investigating Artificial Intelligence and Modern Technologies Enhancement in Stone and Marble Cutting in Palestine

2024

jjmie

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“…Table 2 compares and summarizes the review with our proposed work. Authors applied similar image processing approach in identifying and categorizing marble slabs impurities and streaks with 97.8% accuracy (Kardan Moghaddam et al, 2018). While other researchers applied Kalman filter to count vehicles captured by the camera (Espejel-García et al, 2017), applying Kalman filter to the captured data from the HSI is very slow, since the captured data are in a hypercube format not normal 2D.…”

Section: Namementioning

confidence: 99%

Meibomian gland cyst detection and classification using hyperspectral imaging

et al. 2021

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The primary role of the oily secretion (meibum) is to ensure tear film stability and retard evaporation. In addition to these is providing ocular surface lubrication, which is necessary for smooth eyelid movements. When a gland is blocked, it is described as a Meibomian gland cyst (MGC), which can be a meibomian cyst, usually referred to as chalazion (eye bump), or in the case of inflammation, it is considered to be a hordeolum (sty or stye). Topical ophthalmic ointments and eyelid heat massages can treat early diagnosed MGC; otherwise, surgical operation is required. The current techniques of diagnosing MGC are usually uncomfortable or invasive, such as examining the tarsal plate after everting the eyelid or by biopsy procedures. The purpose of this work is to propose a non-invasive MGC evaluation and classification technique using hyperspectral imaging and image processing. The proposed technique was carried out on a single patient (i.e., case study) for a period of 4 months to monitor the MGC evolution until postsurgery-recovery and was compared with a normal eyelid patient. The collected hypercube data were processed using Multivariate Curve Resolution (MCR) and image analysis to classify the MGC severity levels. The proposed work built the threshold of the complete system, where early diagnosis of an MGC is possible before it becomes visible to the eye, Wessam Shehieb ABOUT THE AUTHOR Wessam Shehieb is an M.Sc. student in Electrical and Electronic Engineering at the Universiti Sains Malaysia and a full-time Teaching Assistant in the Electrical and Computer Engineering Department at Ajman university, UAE. His research interests are medical imaging, intelligent systems, and computational intelligence. Maher Assaad received his Ph.D. degree in EE from the University of Glasgow (U.K.). His current research interests include light sensors and circuits for surface levelness measurement and food quality assessment, wireline data communication circuits for SoC interconnects and optical communication systems. Ayman Tawfik is the head of the Electrical and Computer Engineering Department, Ajman University, UAE. His research interests are digital image processing, VLSI signal processing, digital communication, internet-ofthings and education technology. Nor Ashidi Mat Isa is a Professor and Deputy Dean at the School of Electrical and Electronic Engineering, Universiti Sains Malaysia. His research interests are image processing, intelligent systems, medical imaging and computational intelligence.

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“…Furthermore, having access to smart tools on-site can help reveal the true nature of a product and resolve the misunderstandings between customers and producers upon delivery. An example of such automated visual inspection systems designed for marble is presented in [11]. The advantages of these systems include: 1.…”

Section: Evaluating the Stone Qualitymentioning

confidence: 99%

Sorting Speckled Granites (Nehbandan) and Measuring Their Surface Veining using Machine Vision

Moghaddam*¹,

Rajaei²,

Maraki³

2019

IJRTE

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Quality control and the appearance evaluation of stones are quite challenging in the industry today. The similar appearance of different stones containing the same minerals may result in economic losses, and if the customers fail to identify the type of slates delivered to them correctly, disagreements may arise between the buyers and granite vendors. This study is an attempt toward the automation of surface quality assessment of the Nehbandan(Iran) speckled granite and measurement of the surface patterns under fixed conditions using image processing techniques in order to classify the granite tiles based on their type and amount of impurities and veins. The experimental tests comparing the presented approach with the texture descriptors in the introduced dataset prove the efficiency of the proposed method and its applications under controlled conditions, including the classification of speckled granite tiles with different image resolutions.

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

Cited by 11 publications

References 22 publications

Investigating Artificial Intelligence and Modern Technologies Enhancement in Stone and Marble Cutting in Palestine

Investigating Artificial Intelligence and Modern Technologies Enhancement in Stone and Marble Cutting in Palestine

Meibomian gland cyst detection and classification using hyperspectral imaging

Sorting Speckled Granites (Nehbandan) and Measuring Their Surface Veining using Machine Vision

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