Detecting and measuring rings in banknote images

Various intelligent systems show a rapidly growing potential use of visual information processing technologies. This paper presents an example of employing visual information processing technologies for detecting and measuring rings in banknote images. The size of the rings is one of parameters used to inspect the banknote printing quality. The approach developed consists of two phases. In the first phase, based on histogram processing and data clustering, image areas containing rings are localized and edges of the rings are detected. Then, in the second phase, applying the hard and possibilistic spherical shell clustering to the extracted edge pixels the ring centre and radii are estimated. The experimental investigations performed have shown that even highly occluded rings are robustly detected. Several prototypes of the system developed have been installed in two banknote printing shops in Europe.

[1]  Mauro Barni,et al.  Colour-based detection of defects on chicken meat , 1997, Image Vis. Comput..

[2]  David G. Stork,et al.  Pattern Classification , 1973 .

[3]  Hichem Frigui,et al.  The fuzzy c spherical shells algorithm: A new approach , 1992, IEEE Trans. Neural Networks.

[4]  Giorgio Grasso,et al.  Localization of spherical fruits for robotic harvesting , 2001, Machine Vision and Applications.

[5]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[6]  Azriel Rosenfeld,et al.  Robust detection of straight and circular road segments in noisy aerial images' , 1997, Pattern Recognit..

[7]  Gunther Wyszecki,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition , 2000 .

[8]  Liping Shen,et al.  A method for recognizing particles in overlapped particle images , 2000, Pattern Recognition Letters.

[9]  Geok Soon Hong,et al.  Detection and estimation of circular arc segments , 1995, Pattern Recognit. Lett..

[10]  Masao Hiyane,et al.  A circle-detection algorithm simulating wave propagation , 2005, Machine Vision and Applications.

[11]  Antanas Verikas,et al.  Neural Networks Based Colour Measuring for Process Monitoring and Control in Multicoloured Newspaper Printing , 2000, Neural Computing & Applications.

[12]  Josef Kittler,et al.  A survey of the hough transform , 1988, Comput. Vis. Graph. Image Process..

[13]  Soo-Chang Pei,et al.  Circular arc detection based on Hough transform , 1995, Pattern Recognit. Lett..

[14]  KrishnapuramR.,et al.  The fuzzy c spherical shells algorithm , 1992 .

[15]  KrishnapuramR.,et al.  Robust clustering methods , 1997 .

[16]  José Luis Pons Rovira,et al.  Automatic fruit recognition: a survey and new results using Range/Attenuation images , 1999, Pattern Recognit..

[17]  José Luis Pons Rovira,et al.  Machine Vision and Applications Manuscript-nr. a Vision System Based on a Laser Range--nder Applied to Robotic Fruit Harvesting , 2022 .

[18]  Pär Kierkegaard,et al.  A method for detection of circular arcs based on the Hough transform , 1992, Machine Vision and Applications.

[19]  Clement T. Yu,et al.  Segmentation of skin cancer images , 1999, Image Vis. Comput..

[20]  Rajesh N. Davé,et al.  Robust clustering methods: a unified view , 1997, IEEE Trans. Fuzzy Syst..

[21]  Kazuhiro Nakano,et al.  Application of neural networks to the color grading of apples , 1997 .

[22]  Antanas Verikas,et al.  Monitoring the De-Inking Process through Neural Network-Based Colour Image Analysis , 2000, Neural Computing & Applications.

[23]  Hichem Frigui,et al.  Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation. II , 1995, IEEE Trans. Fuzzy Syst..

[24]  James M. Keller,et al.  A possibilistic approach to clustering , 1993, IEEE Trans. Fuzzy Syst..