Detection of bubbles as concentric circular arrangements

The paper proposes a method for the detection of bubble-like transparent objects in a liquid. The detection problem is non-trivial since bubble appearance varies considerably due to different lighting conditions causing contrast reversal and multiple interreflections. We formulate the problem as the detection of concentric circular arrangements (CCA). The CCAs are recovered in a hypothesize-optimize-verify framework. The hypothesis generation is based on sampling from the partially linked components of the non-maximum suppressed responses of oriented ridge filters, and is followed by the CCA parameter estimation. Parameter optimization is carried out by minimizing a novel cost-function. The performance was tested on gas dispersion images of pulp suspension and oil dispersion images. The mean error of gas/oil volume estimation was used as a performance criterion due to the fact that the main goal of the applications driving the research was the bubble volume estimation. The method achieved 28 and 13 % of gas and oil volume estimation errors correspondingly outperforming the OpenCV Circular Hough Transform in both cases and the WaldBoost detector in gas volume estimation.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Erik H. W. Meijering,et al.  Cell Segmentation: 50 Years Down the Road [Life Sciences] , 2012, IEEE Signal Processing Magazine.

[3]  Mei Xie,et al.  Fast and Robust Circular Object Detection With Probabilistic Pairwise Voting , 2011, IEEE Signal Processing Letters.

[4]  Ajith Abraham,et al.  Automatic circle detection on digital images with an adaptive bacterial foraging algorithm , 2010, Soft Comput..

[5]  Tuomas Eerola,et al.  Comparison of Appearance-Based and Geometry-Based Bubble Detectors , 2014, ICCVG.

[6]  V. F. Leavers,et al.  Which Hough transform , 1993 .

[7]  Douglas W. Reeve,et al.  Pulp bleaching : principles and practice , 1996 .

[8]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[9]  Raúl Enrique Sánchez-Yáñez,et al.  Circle detection on images using genetic algorithms , 2006, Pattern Recognit. Lett..

[10]  Qing Wang,et al.  Fast and robust segmentation of spherical particles in volumetric data sets from brightfield microscopy , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  J. Kittler,et al.  Comparative study of Hough Transform methods for circle finding , 1990, Image Vis. Comput..

[12]  Gabriel Corkidi,et al.  Automated recognition of oil drops in images of multiphase dispersions via gradient direction pattern , 2011, 2011 4th International Congress on Image and Signal Processing.

[13]  Yu-Sun Liu,et al.  Polling an image for circles by random lines , 2003 .

[14]  B. Taboada,et al.  Hough transform based method for air bubbles and oil drops segmentation in dispersions occurring in stirred bioreactors , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[15]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[16]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[17]  Erkki Oja,et al.  Probabilistic and non-probabilistic Hough transforms: overview and comparisons , 1995, Image Vis. Comput..

[18]  Wenhui Zhang,et al.  A method for recognizing overlapping elliptical bubbles in bubble image , 2012, Pattern Recognit. Lett..

[19]  Jiri Matas,et al.  WaldBoost - learning for time constrained sequential detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[20]  Ronny Hänsch,et al.  Automated drop detection using image analysis for online particle size monitoring in multiphase systems , 2012, Comput. Chem. Eng..

[21]  Nataliya Strokina,et al.  Machine vision methods for process measurements in pulping , 2013 .

[22]  J. Alison Noble,et al.  Toward a method for automatic grading of microscope human embryo images , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[23]  Tony Lindeberg,et al.  Edge Detection and Ridge Detection with Automatic Scale Selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[24]  Lin Xia,et al.  Bubble Image Segmentation of Gas/Liquid Two-Phase Flow Based on Improved Canny Operator , 2008, CSSE 2008.

[25]  Darren J. Kerbyson,et al.  Size invariant circle detection , 1999, Image Vis. Comput..

[26]  E. Meijering.  Cell Segmentation : 50 Years Down the Road , 2012 .

[27]  Kamil Riha,et al.  Circle detection in pulsative medical video sequence , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[28]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[29]  B. Taboada,et al.  Semi-automatic image analysis methodology for the segmentation of bubbles and drops in complex dispersions occurring in bioreactors , 2006 .

[30]  Stella X. Yu,et al.  Finding dots: Segmentation as popping out regions from boundaries , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  Changming Sun,et al.  Splitting touching cells based on concave points and ellipse fitting , 2009, Pattern Recognit..

[32]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[33]  C O S Sorzano,et al.  Quantitative morphometrical characterization of human pronuclear zygotes. , 2008, Human reproduction.

[34]  Qi Wu,et al.  Raindrop detection and removal using salient visual features , 2012, 2012 19th IEEE International Conference on Image Processing.

[35]  Xenophon Zabulis,et al.  Detection of densely dispersed spherical bubbles in digital images based on a template matching technique Application to wet foams , 2007 .

[36]  I. Vaughan L. Clarkson,et al.  Maximum-likelihood estimation of circle parameters via convolution , 2006, IEEE Transactions on Image Processing.

[37]  William H. Press,et al.  Book-Review - Numerical Recipes in Pascal - the Art of Scientific Computing , 1989 .

[38]  Nicolas Pérez de la Blanca,et al.  Applying deformable templates for cell image segmentation , 2000, Pattern Recognit..