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Colour tonality inspection using eigenspace features

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Abstract

In industrial quality inspection of colour texture surfaces, such as ceramic tiles or fabrics, it is important to maintain a consistent colour shade or tonality during production. We present a multidimensional histogram method using a novelty detection scheme to inspect the surfaces. The image noise, introduced by the imaging system, is found mainly to affect the chromatic channels. For colour tonality inspection, the difference between images is very subtle and comparison in the noise dominated chromatic channels is error prone. We perform vector-ordered colour smoothing and extract a localised feature vector at each pixel. The resulting histogram represents an encapsulation of local and global information. Principal component analysis (PCA) is performed on this multidimensional feature space of an automatically selected reference image to obtain reliable colour shade features, which results in a reference eigenspace. Then unseen product images are projected onto this eigenspace and compared for tonality defect detection using histogram comparison. The proposed method is compared and evaluated on a data set with groundtruth.

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Authors and Affiliations

  1. Department of Computer Science, University of Bristol, Bristol, BS8 1UB, England

    Xianghua Xie, Majid Mirmehdi & Barry Thomas

Authors
  1. Xianghua Xie
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  2. Majid Mirmehdi
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  3. Barry Thomas
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Corresponding author

Correspondence to Xianghua Xie.

Additional information

Xianghua Xie is currently a Ph.D. student and a research assistant in the Department of Computer Science, University of Bristol, U.K. Prior to this, he received an M.Sc. degree in advanced computing with commendation from the University of Bristol in 2002 and a B.Sc. degree in environmental engineering from the Tongji University, Shanghai, P.R. China, in 2000. His current research interests are texture analysis, image segmentation, surface inspection, deformable models and historical document analysis. He is a student member of the BMVA, the IEE and the IEEE.

Majid Mirmehdi received the B.Sc. (Hons.) and Ph.D. degrees in computer science in 1985 and 1991 respectively, from the City University, London. He has worked both in industry and in academia. He is currently a Reader in the Department of Computer Science at the University of Bristol, UK. His research interests include texture analysis, colour image analysis, medical imaging and document recognition. He has over 100 refereed conference and journal publications in these areas. He is an associate editor of the Pattern Analysis and Applications Journal. He is a member of the IEE, IEEE and a member and the Chairman of the British Machine Vision Association.

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Xie, X., Mirmehdi, M. & Thomas, B. Colour tonality inspection using eigenspace features. Machine Vision and Applications 16, 364–373 (2006). https://doi.org/10.1007/s00138-005-0008-9

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