Perceptual correction for colour grading of random textures

Boukouvalas, C.; Petrou, Maria

doi:10.1007/s001380050131

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…This allowed the a priori definition of thresholds for colour tonality. In [14], the authors applied perceptual smoothing before colour tonality inspection. In [150], Xie and Mirmehdi further explored by incorporating local chromatic features to discriminate subtle colour tonality difference.…”

Section: Tonality Inspectionmentioning

confidence: 99%

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A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques

Xie

2008

ELCVIA

407

219

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In this paper, we systematically review recent advances in surface inspection using computer vision and image processing techniques, particularly those based on texture analysis methods. The aim is to review the state-of-the-art techniques for the purposes of visual inspection and decision making schemes that are able to discriminate the features extracted from normal and defective regions. This field is so vast that it is impossible to cover all the aspects of visual inspection. This paper focuses on a particular but important subset which generally treats visual surface inspection as texture analysis problems. Other topics related to visual inspection such as imaging system and data acquisition are out of the scope of this survey. The surface defects are loosely separated into two types. One is local textural irregularities which is the main concern for most visual surface inspection applications. The other is global deviation of colour and/or texture, where local pattern or texture does not exhibit abnormalities. We refer this type of defects as shade or tonality problem. The second type of defects have been largely neglected until recently, particularly when colour imaging system has been widely used in visual inspection and where chromatic consistency plays an important role in quality control. The emphasis of this survey though is still on detecting local abnormalities, given the fact that majority of the reported works are dealing with the first type of defects. The techniques used to inspect textural abnormalities are discussed in four categories, statistical approaches, structural approaches, filter based methods, and model based approaches, with a comprehensive list of references to some recent works. Due to rising demand and practice of colour texture analysis in application to visual inspection, those works that are dealing with colour texture analysis are discussed separately. It is also worth noting that processing vector-valued data has its unique challenges, which conventional surface inspection methods have often ignored or do not encounter. We also compare classification approaches with novelty detection approaches at the decision making stage. Classification approaches often require supervised training and usually provide better performance than novelty detection based approaches where training is only carried out on defect-free samples. However, novelty detection is relatively easier to adapt and is particularly desirable when training samples are incomplete.

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Section: Tonality Inspectionmentioning

confidence: 99%

“…These studies suggested that global measurements, particularly colour histograms and their related statistics are useful in colour tonality defect detection. Smoothing to reduce noise interference (prehistogram computation) has also been found beneficial in colour tonality discrimination [14,150].…”

Section: Tonality Inspectionmentioning

confidence: 99%

A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques

Xie

2008

ELCVIA

407

219

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“…Image processing techniques have been used in various fields to perform automated quality control without human intervention. These techniques are in use and becoming popular in the quality control of products such as textile [3][4][5][6], ceramic [7][8][9], steel [10,11], glass [12], electronic circuits [13], weld bead [14] , metal lids [15] and cylindrical parts [16] to keep up with high quality standards set by the competitive markets. There are also academic studies [17][18][19][20][21][22] on applying image processing techniques in the quality control of wooden surfaces.…”

Section: Introductionmentioning

confidence: 99%

A Quality Control System Prototype for Detecting Knot Defects in the Wooden Panel Manufacturing

Kılıç

Susuz²,

Süzek

2019

Mugla Journal of Science and Technology

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Product quality has become a necessary goal for all manufacturers in today's competitive market. Product defects, not detected, cause financial damages and reputation loss for the manufacturer. These defects can be due to quality of the inputs or misuse of the good quality inputs during the manufacturing process. This is also the case for wooden panel manufacturing where elements are the basic input. It is possible to reduce the loss of the manufacturer by using a method that minimizes the human error in the inspection of the elements. In this study, we, first, identified the quality control problems of the wooden panel manufacturers and basic steps in the automated element quality control. We then developed a prototype for the detection of knots, the most common defects in wooden panels. This prototype, with 80.0% true positive (with knot defect) and 82.0% true negative (without knot defect) rates, performs close to accuracy rates of a quality control inspector. The element image library created during the development of the system made publicly available for use in similar studies. This prototype is expected to be developed to detect other wood defects and to be applied in the wooden panel manufacturing.

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“…Almost all the methods revised, like (Boukouvalas et al, 1999), (Boukouvalas and Petrou, 2000), (López et al, 2001) and (López et al, 2005) detect chromatic defects but not topographical ones. In (Smith, 2000) a technique that is able to isolated mixed topographic and chromatic surface features has been developed.…”

Section: Introductionmentioning

confidence: 99%

3d Inspection System in Ceramic Tiles Surfaces With Range Images

Rodríguez¹,

Andreu-Garcı́a²,

Rodas-Jordá³

et al. 2010

Proceedings of the International Conference on Computer Vision Theory and Applications

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In this paper we propose a system to characterize 3D defects of range images, which can be combined with traditional surface inspection methods in an industrial environment for ceramic tiles inspection. Our application has the advantage of learning the geometric features of the ceramic pieces, creating a unique 3D model against which we compare the test pieces. In addition to this, the system includes a robust learning phase, which discards tiles with defects impossible to see from a human expert and a more stringent inspection in areas with low uncertainty. Experiments with real data were performed. Our data consist of tiles of different types, shapes and silk-screen of ceramic tiles. Results are promising for tiles with a straight orientation, over 99 % of defects are correctly classified.

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Perceptual correction for colour grading of random textures

Cited by 12 publications

References 9 publications

A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques

A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques

A Quality Control System Prototype for Detecting Knot Defects in the Wooden Panel Manufacturing

3d Inspection System in Ceramic Tiles Surfaces With Range Images

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