caa a22 4500 46793665X CHVBK 20180406152959.0 cr unu---uuuuu 170328e20060201xx s 000 0 eng 10.1007/s00138-005-0008-9 doi (NATIONALLICENCE)springer-10.1007/s00138-005-0008-9 Colour tonality inspection using eigenspace features [Elektronische Daten] [Xianghua Xie, Majid Mirmehdi, Barry Thomas] 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. Springer-Verlag, 2005 Colour tonality nationallicence Surface inspection nationallicence Image noise analysis nationallicence Vector directional processing nationallicence Multidimensional histogramming nationallicence Xie Xianghua Department of Computer Science, University of Bristol, BS8 1UB, Bristol, England aut Mirmehdi Majid Department of Computer Science, University of Bristol, BS8 1UB, Bristol, England aut Thomas Barry Department of Computer Science, University of Bristol, BS8 1UB, Bristol, England aut Machine Vision and Applications Springer-Verlag 16/6(2006-02-01), 364-373 0932-8092 16:6<364 2006 16 138 https://doi.org/10.1007/s00138-005-0008-9 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00138-005-0008-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Xie Xianghua Department of Computer Science, University of Bristol, BS8 1UB, Bristol, England aut NATIONALLICENCE 700 1- Mirmehdi Majid Department of Computer Science, University of Bristol, BS8 1UB, Bristol, England aut NATIONALLICENCE 700 1- Thomas Barry Department of Computer Science, University of Bristol, BS8 1UB, Bristol, England aut NATIONALLICENCE 773 0- Machine Vision and Applications Springer-Verlag 16/6(2006-02-01), 364-373 0932-8092 16:6<364 2006 16 138 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer