S. Yotte¹, J. Riss, D. Breysse, S. Ghosh

CMES-Computer Modeling in Engineering & Sciences, Vol.5, No.2, pp. 171-188, 2004, DOI:10.3970/cmes.2004.005.171

Abstract Particle distribution influences the particulate reinforced metal matrix composites (PMMC). The knowledge of particle distribution is essential for material design. The study of particle distribution relies on analysis of material images. In this paper three methods are used on an image of an Al/SiC composite. The first method consists in applying successive dilations to the image. At each step the number of objects and the total object area are determined. The decrease of the number of objects as a function of the area is an indicator of characteristic distances. The second method is based on dilations of one particle among… More >

Lili He^1,2, Dantong Ouyang^1,2, Meng Wang^1,2, Hongtao Bai^1,2, Qianlong Yang^1,2, Yaqing Liu^3,4, Yu Jiang^1,2,*

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 549-570, 2018, DOI:10.32604/cmc.2018.03840

Abstract In this paper, the clustering analysis is applied to the satellite image segmentation, and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing power. The method firstly adopts the fuzzy C-means clustering (FCM) to obtain the satellite cloud image segmentation. Secondly, in the cloud image, we dispose the ‘high-density connected’ pixels in the same cloud clusters and the ‘low-density connected’ pixels in different cloud clusters. Therefore, we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster knowledge. Finally, using the method of spectral threshold recognition and… More >