Original Paper

Machine Vision and Applications

, Volume 16, Issue 6, pp 364-373

First online:

Colour tonality inspection using eigenspace features

  • Xianghua XieAffiliated withDepartment of Computer Science, University of Bristol Email author 
  • , Majid MirmehdiAffiliated withDepartment of Computer Science, University of Bristol
  • , Barry ThomasAffiliated withDepartment of Computer Science, University of Bristol

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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.


Colour tonality Surface inspection Image noise analysis Vector directional processing Multidimensional histogramming