Color grading of randomly textured ceramic tiles using color histograms

Boukouvalas, C.; Kittler, Josef; Mařík, Radek; Petrou, Maria

doi:10.1109/41.744415

Cited by 64 publications

(54 citation statements)

References 5 publications

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“…However, following a detailed search on the specialised literature we were able to find a large number of approaches that have been proposed to address the automatic inspection of ceramic tiles [3,[8][9][10][11]13,23,24], which are products that have several common characteristics with painted slates. Thus, although the manufacturing processes for slates and ceramic tiles are different, both products have rectangular shapes, have textured surfaces and they are transported at the inspection point via conveying means [2].…”

Section: $2 Related Vision-based Inspection Systemsmentioning

confidence: 99%

“…The use of line scan cameras has alleviated to some extent the requirement to devise complex illumination arrangements, as the light intensity has to be maintained uniform only along a narrow stripe area (that is imaged by the line-scan sensors). To this end, the ceramic inspection systems examined in our review employed either diffuse [8][9][10][11][12] or collimated lighting set-ups [1,2,[11][12].…”

Section: $2 Related Vision-based Inspection Systemsmentioning

confidence: 99%

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A Machine Vision System for Quality Grading of Painted Slates

Ghita

Carew

Whelan

2012

Machine Vision Handbook

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The major aim of this chapter is to detail the technology associated with a novel industrial inspection system that is able to robustly identify the visual defects present on the surface of painted slates. The development of a real-time automated slate inspection system proved to be a challenging task since the surface of the slate is painted with glossy dark colours, the slate is characterised by depth profile non-uniformities and it is transported at the inspection line via high-speed conveyors. In order to implement an industrial compliant system, in our design we had to devise a large number of novel solutions including the development of a full customised illumination set-up and the development of flexible image-processing procedures that can accommodate the large spectrum of visual defects that can be present on the slate surface and the vibrations generated by the slate transport system. The developed machine vision system has been subjected to a thorough robustness evaluation and the reported experimental results indicate that the proposed solution can be used to replace the manual procedure that is currently used to grade the painted slates in manufacturing environments.

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Section: $2 Related Vision-based Inspection Systemsmentioning

confidence: 99%

Section: $2 Related Vision-based Inspection Systemsmentioning

confidence: 99%

A Machine Vision System for Quality Grading of Painted Slates

Ghita

Carew

Whelan

2012

Machine Vision Handbook

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“…This is motivated by advances in imaging and processing technologies and the proliferation of colour cameras. Colour has been used in the development of algorithms that have been applied to many applications including object recognition 21,22 , skin detection 23 , image retrieval 24,25,26 and product inspection 27,28 . Many of the existing colour segmentation techniques are based on simple colour partitioning (clustering) techniques and their performance is appropriate only if the colour information is locally homogenous.…”

Section: Colour Analysismentioning

confidence: 99%

“…According to Skarbek and Koschan 30 the pixel based colour segmentation techniques can be further divided into two main categories: histogram-thresholding segmentation and colour clustering techniques. The histogram-based segmentation techniques attempt to identify the peaks in the colour histogram 21,27,31,32,33 and in general provide a coarse segmentation that is usually the input for more sophisticated techniques. Clustering techniques have been widely applied in practice to perform image segmentation 34 .…”

Section: Colour Analysismentioning

confidence: 99%

Colour Texture Analysis

Whelan

Ghita

2008

Handbook of Texture Analysis

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This chapter presents a novel and generic framework for image segmentation using a compound image descriptor that encompasses both colour and texture information in an adaptive fashion. The developed image segmentation method extracts the texture information using low-level image descriptors (such as the Local Binary Patterns (LBP)) and colour information by using colour space partitioning. The main advantage of this approach is the analysis of the textured images at a micro-level using the local distribution of the LBP values, and in the colour domain by analysing the local colour distribution obtained after colour segmentation. The use of the colour and texture information separately has proven to be inappropriate for natural images as they are generally heterogeneous with respect to colour and texture characteristics. Thus, the main problem is to use the colour and texture information in a joint descriptor that can adapt to the local properties of the image under analysis. We will review existing approaches to colour and texture analysis as well as illustrating how our approach can be successfully applied to a range of applications including the segmentation of natural images, medical imaging and product inspection.

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“…Meanwhile, the well-rounder technique and successful experiences of foreign countries not get practical application because china clay burden, production process and process control in country situation difference as in [1], [2]. Therefore developing an automated color classification algorithm is of great importance, which can effectively resolve the problems brought by manual activity, and can help to improve the full automation in the tile industry and advance the development of automated color recognition technology in other related industries as in [3].…”

Section: Introductionmentioning

confidence: 99%

A statistical features-based color difference classification method

Feng-wu

Jiang

2013

2013 25th Chinese Control and Decision Conference (CCDC)

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This paper presents a method for color difference based on Histogram statistical values. First, the color image in RGB color space is converted to HSI color space, median filter was selected for image preprocessing.Then, the ceramic samples HSI Histogram statistical of each channel was calculated respectively. Take the Histogram statistical as the color difference classification character value. For real time requirement, using minimum distance classifier as classification basis. Compared with the S, I channel, the results showed that adopted the H channel Histogram statistical value as feature vector has higher accuracy for ceramic tile color difference classification.

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Color grading of randomly textured ceramic tiles using color histograms

Cited by 64 publications

References 5 publications

A Machine Vision System for Quality Grading of Painted Slates

A Machine Vision System for Quality Grading of Painted Slates

Colour Texture Analysis

A statistical features-based color difference classification method

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