3-D Granulometry Using Image Processing

Image-based methods for estimating the particle size distribution (granulometry) usually analyze two-dimensional (2-D) samples of particles disposed on a conveyor belt. Such approaches have to deal with occlusions and cannot evaluate the thickness of each particle. Three-dimensional (3-D) vision systems can reduce the acquisition constraints and speed up the quality control process. This paper proposes a novel 3-D vision system for analyzing the granulometry of falling particles. The system is designed to work in real time and to compute a partial 3-D reconstruction of the particle from a single pair of two-view images, which is then enhanced by using a neural-based technique. The validation of the proposed approach has been performed by considering three application scenarios for which the system achieved satisfactory accuracy and robustness.

[1]  Jae-Joon Song,et al.  Statistical estimation of blast fragmentation by applying stereophotogrammetry to block piles , 2014 .

[2]  Barrie Hayes-Gill,et al.  Image Segmentation of Overlapping Particles in Automatic Size Analysis Using Multi-Flash Imaging , 2007, 2007 IEEE Workshop on Applications of Computer Vision (WACV '07).

[3]  Mohammad Sadeghi,et al.  Size Distribution Estimation of Stone Fragments via Digital Image Processing , 2010, ISVC.

[4]  Janne Heikkilä,et al.  A four-step camera calibration procedure with implicit image correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Héctor Rabal,et al.  Characterization of a paint drying process through granulometric analysis of speckle dynamic patterns , 2010, Signal Process..

[7]  Rafael C. González,et al.  Digital image processing, 3rd Edition , 2008 .

[8]  Robert Lanouette,et al.  Wood chip physical quality definition and measurement , 2005 .

[9]  V. Piuri,et al.  Image processing for granulometry analysis via neural networks , 2008, 2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.

[10]  Weixing Wang.  Image Analysis of Grains from A Falling Stream , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[11]  Guillermo Ayala,et al.  A granulometric analysis of specular microscopy images of human corneal endothelia , 2005, Comput. Vis. Image Underst..

[12]  Italo Onederra,et al.  Measuring blast fragmentation at Esperanza mine using high-resolution 3D laser scanning , 2015 .

[13]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[14]  Wei Chen,et al.  Particle shape characterisation and its application to discrete element modelling , 2014 .

[15]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[16]  N. Sandler,et al.  Rapid Particle Size Measurement Using 3D Surface Imaging , 2011, AAPS PharmSciTech.

[17]  Vincenzo Piuri,et al.  Design of an Automatic Wood Types Classification System by Using Fluorescence Spectra , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[18]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[19]  Lin-Sien H. Lum,et al.  NIST Recommended Practice Guide: Particle Size Characterization , 2001 .

[20]  Augusto Sarti,et al.  Accurate and simple geometric calibration of multi-camera systems , 1999, Signal Process..

[21]  Vincenzo Piuri,et al.  Improving OSB wood panel production by vision-based systems for granulometric estimation , 2015, 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI).

[22]  Hamed Sari-Sarraf,et al.  Machine Vision System for Characterizing Horizontal Wicking and Drying Using an Infrared Camera , 2016, IEEE Transactions on Industrial Informatics.

[23]  Dan Ionescu,et al.  3D object model recovery from 2D images using structured light , 2004, IEEE Transactions on Instrumentation and Measurement.

[24]  Takeo Kanade,et al.  A Multiple-Baseline Stereo , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Joanna Kozakiewicz,et al.  Image analysis algorithm for detection and measurement of Martian sand grains , 2018, Earth Science Informatics.

[26]  Michael Nielsen,et al.  Vision-Based 3D Peach Tree Reconstruction for Automated Blossom Thinning , 2012, IEEE Transactions on Industrial Informatics.

[27]  Alberto Boschetto,et al.  Powder sampling and characterization by digital image analysis , 2012 .

[28]  Henk G. Merkus,et al.  Particle Size Measurements: Fundamentals, Practice, Quality , 2009 .

[29]  Shengyong Chen,et al.  Finding Optimal Focusing Distance and Edge Blur Distribution for Weakly Calibrated 3-D Vision , 2013, IEEE Transactions on Industrial Informatics.

[30]  Michele Nappi,et al.  Hybrid Computational Intelligence for Ambient Intelligent Environments , 2005, AWIC.

[31]  Wilfried Philips,et al.  Quantitative Image Analysis with Mathematical Morphology , 2002 .

[32]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[33]  Stina Frosch,et al.  Image Analysis of Pellet Size for a Control System in Industrial Feed Production , 2011, PloS one.

[34]  Marcel Brun,et al.  Characterization of bio-dynamic speckles through classical and fuzzy mathematical morphology tools , 2013, Signal Process..