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.
3-D granulometry using image processing / R. Donida Labati, A. Genovese, E. Munoz Ballester, V. Piuri, F. Scotti. - In: IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS. - ISSN 1551-3203. - 15:3(2019), pp. 1251-1264.
|Titolo:||3-D granulometry using image processing|
|Parole Chiave:||Falling particles; granulometry; image processing; 3-D reconstruction|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
|Data di pubblicazione:||2019|
|Data ahead of print / Data di stampa:||16-lug-2018|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/TII.2018.2856466|
|Appare nelle tipologie:||01 - Articolo su periodico|