Paper
12 July 2011 Recognizing overlapped particles during a crystallization process from in situ video images for measuring their size distributions
Ola Suleiman Ahmad, Johan Debayle, Nesrine Gherras, Benoît Presles, Gilles Févotte, Jean-Charles Pinoli
Author Affiliations +
Proceedings Volume 8000, Tenth International Conference on Quality Control by Artificial Vision; 800005 (2011) https://doi.org/10.1117/12.889915
Event: 10th International Conference on Quality Control by Artificial Vision, 2011, Saint-Etienne, France
Abstract
This paper presents a method to recognize polygonal-shaped particles (i.e. rectangles, regular/irregular prisms) among agglomerated crystals from in situ images during a crystallization process. The aim is to measure the particle size distributions (PSD), which are key measurements needed for the purification operations and the quality control of chemical products or drugs. The method is first based on detecting the geometric features of the particles identified by their salient corners. A clustering technique is then applied by grouping three correspondent salient corners belonging to the same polygon. The efficiency of the proposed method is tested on particles of Ammonium Oxalate during batch crystallization in pure water. Particle size distributions are calculated, and a quantitative comparison between automatic and manual sizing is performed.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ola Suleiman Ahmad, Johan Debayle, Nesrine Gherras, Benoît Presles, Gilles Févotte, and Jean-Charles Pinoli "Recognizing overlapped particles during a crystallization process from in situ video images for measuring their size distributions", Proc. SPIE 8000, Tenth International Conference on Quality Control by Artificial Vision, 800005 (12 July 2011); https://doi.org/10.1117/12.889915
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CITATIONS
Cited by 4 scholarly publications.
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KEYWORDS
Particles

Crystals

Image processing

Image segmentation

Solids

Corner detection

Video

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