A Simple and Efficient Method for Ceramic Tile Surface Defects Detection

Hocenski, Željko; Keser, Tomislav; Baumgartner, Alfonzo

doi:10.1109/isie.2007.4374844

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…Moving average with local difference method finds dot shaped defects on ceramic tile surface image [6], [7], [8]. MALD considers a symmetric 1D kernel, c.f.…”

Section: Mald Methods With Integral Image Approachmentioning

confidence: 99%

“…In order to get the mean value directly from the summations contained in II 1D , Alg. 1 (line 4) can be replaced with (6), where summations are divided with the number of pixels for the mean value calculation.…”

Section: Mald With 1d Integral Image Approachmentioning

confidence: 99%

“…Calculation of (1) uses 2w '+' and 1 '-' operation for N x kernel locations. Second type MALD 2 uses current kernel pixel sum, at location (x,y), in order to calculate the sum of pixels at the next kernel location (x+1,y), as it is implemented in [6]. MALD 2 uses moderate number of ALU operations, c.f.…”

Section: Implementationsmentioning

confidence: 99%

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CPU, GPU and FPGA Implementations of MALD: Ceramic Tile Surface Defects Detection Algorithm

2014

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Original scientific paperThis paper addresses adjustments, implementation and performance comparison of the Moving Average with Local Difference (MALD) method for ceramic tile surface defects detection. Ceramic tile production process is completely autonomous, except the final stage where human eye is required for defects detection. Recent computational platform development and advances in machine vision provides us with several options for MALD algorithm implementation. In order to exploit the shortest execution time for ceramic tile production process, the MALD method is implemented on three different platforms: CPU, GPU and FPGA, and it is implemented on each platform in at least two ways. Implementations are done in MATLAB's MEX/C++, C++, CUDA/C++, VHDL and Assembly programming languages. Execution times are measured and compared for different algorithms and their implementations on different computational platforms.Key words: CUDA, FPGA, GPU, Integral Image, MALD, Ceramic Tile CPU, GPU i FPGA implementacija MALD algoritma za otkrivanje nepravilnosti na površini keramičkih pločica. U ovom radu razmatra se prilagodba, implementacija i usporedba performansi metode pomičnog usrednjavanja s lokalnom diferencijom (MALD) s primjenom u otkrivanju površinskih nedostataka na keramičkim ploči-cama. Proizvodna linija keramičkih pločica je autonomna sve do zadnje faze u kojoj je potreban ljudski vid kako bi se otkrili eventualni nedostaci na keramičkim pločicama. Nedavnim razvojem računalnih platformi i razvojem metoda računalnog vida omogućena je implementacija MALD metode na nekoliko načina. U nastojanju skraćenja vremena potrebnog za proizvodnju keramičkih pločica, MALD metoda je implementirana u trima različitim platformama: CPU (central processing unit), GPU (graphic processing unit) i FPGA (field programmable gate array), te s barem dva različita algoritma. Implementacija je izvršena sa MATLAB MEX/C++, C++, CUDA/C++, VHDL te Asembler programskim jezicima. Izmjerena vremena obrade su meusobno usporeena za različite algoritme i njihove implementacije na različitim računalnim platformama.

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“…Moving average with local difference method finds dot shaped defects on ceramic tile surface image [6], [7], [8]. MALD considers a symmetric 1D kernel, c.f.…”

Section: Mald Methods With Integral Image Approachmentioning

confidence: 99%

Section: Mald With 1d Integral Image Approachmentioning

confidence: 99%

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CPU, GPU and FPGA Implementations of MALD: Ceramic Tile Surface Defects Detection Algorithm

2014

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“…Pre-processing operations include RGB to Binary conversion, image enhancement, noise reduction etc. Pre-processing operations are imperative for renovating the captured RGB image [7].…”

Section: Firstmentioning

confidence: 99%

An Enhanced Automatic Surface and Structural Flaw Inspection and Categorization using Image Processing Both for Flat and Textured Ceramic Tiles

Islam¹,

Sahriar²

2012

IJCA

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As the price of ceramic tiles depend on its limpidness and precision of surface texture, color and shape, it's in fact a great challenge to control surface eminence and uphold production rate in the field of industrial fabrication of ceramic tiles. Under consideration these criteria, in this research paper, we have proposed an enhanced automatic surface flaw detection and categorization procedure that is able to guarantee the quality of ceramic tiles as well as production rate in industrial fabrication. Our proposed model plays an important role for automatic revealing of surface flaw during production and packaging. This proposed model includes an automatic categorization technique using computer vision that helps us to make sense about the pattern of surface defect within a very short time and also helps to make quick decision about the recovery process. Moreover, it also ensures the quality of tiles automatically during packaging procedure so that the defected tiles may not be mixed up with the fresh tiles.

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“…Then the classification is made by feed-forward neural network classifier. An efficient and simple method for ceramic tile surface defects is presented in [6]. The image preprocessing is made by intensity adjustment, contrast, image acquisition and surface extraction technique.…”

Section: Introductionmentioning

confidence: 99%

Surface Grading Using Deep Learning Approach for Ceramic Tiles

Raja¹

2018

International Journal of Advances in Signal and Image Sciences

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The surface grading of ceramic tiles is essential for ceramic tile industries due to the huge development of infrastructure and essential usage of ceramic tiles. In some industries, surface grading is performed manually. It is a difficult task due to a large number of variations in the surface properties. In this study, a technique for surface grading of ceramic using deep learning is presented. The system uses the VxC Tiles of Surface Grading (TSG) database for performance evaluation. The deep learning based Convolution Neural Network (CNN) is used for the surface grading approach that classifies the tiles into Grade-1 (G1), Grade-2 (G2) and Grade-3 (G3). The system uses seven layers in CNN, which includes convolution, pooling and fully connected layers. Initially, the input tile image is converted Red, Green and Blue (RGB) color channels, and then CNN approach is applied for the classification of tile images. Experimental results show the better classification accuracy of 96.17% for surface grading of ceramic tiles using a deep learning approach.

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A Simple and Efficient Method for Ceramic Tile Surface Defects Detection

Cited by 16 publications

References 8 publications

CPU, GPU and FPGA Implementations of MALD: Ceramic Tile Surface Defects Detection Algorithm

CPU, GPU and FPGA Implementations of MALD: Ceramic Tile Surface Defects Detection Algorithm

An Enhanced Automatic Surface and Structural Flaw Inspection and Categorization using Image Processing Both for Flat and Textured Ceramic Tiles

Surface Grading Using Deep Learning Approach for Ceramic Tiles

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