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Indexed by:会议论文

Date of Publication:2017-01-01

Included Journals:EI、CPCI-S、Scopus

Page Number:11515-11520

Key Words:image analysis; agglomeration; texture feature; re-segmentation

Abstract:A synthetic image analysis method is proposed for in-situ detection of particle agglomeration for monitoring crystallization processes, based on using a non-invasive imaging system. The proposed method consists of image pre-processing, feature analysis, shape identification, and re-segmentation. Firstly, in-situ captured images are pre-processed to eliminate the influence from uneven illumination background and particle motion. Then, based on choosing the fundamental image features of needle-like crystals, a texture computation algorithm is established with a gray level co-occurrence matrix (GLCM) defined for different particle types. Subsequently, a shape identification algorithm is given to distinguish the primary particles from overlapped particles in a captured image. Finally, a re-segmentation algorithm is constructed to separate individual crystals from the overlapped crystals, by using a geometric approach and the chord-to-point distance accumulation (CPDA) technique, and then pseudo agglomerates are recognized from the overlapped crystals based on the texture analysis. Experimental results on the cooling crystallization of beta form L-glutamic acid well demonstrate the effectiveness of the proposed image analysis method.