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dc.contributor.authorChiwoo Park,
dc.contributor.authorHuang, J. Z.
dc.contributor.authorJi, J. X.
dc.contributor.authorYu Ding,
dc.date.accessioned2016-02-28T05:53:32Z
dc.date.available2016-02-28T05:53:32Z
dc.date.issued2013-03
dc.identifier.citationChiwoo Park, Huang JZ, Ji JX, Yu Ding (2013) Segmentation, Inference and Classification of Partially Overlapping Nanoparticles. IEEE Trans Pattern Anal Mach Intell 35: 1–1. Available: http://dx.doi.org/10.1109/tpami.2012.163.
dc.identifier.issn0162-8828
dc.identifier.issn2160-9292
dc.identifier.pmid22848127
dc.identifier.doi10.1109/tpami.2012.163
dc.identifier.urihttp://hdl.handle.net/10754/599569
dc.description.abstractThis paper presents a method that enables automated morphology analysis of partially overlapping nanoparticles in electron micrographs. In the undertaking of morphology analysis, three tasks appear necessary: separate individual particles from an agglomerate of overlapping nano-objects; infer the particle's missing contours; and ultimately, classify the particles by shape based on their complete contours. Our specific method adopts a two-stage approach: the first stage executes the task of particle separation, and the second stage conducts simultaneously the tasks of contour inference and shape classification. For the first stage, a modified ultimate erosion process is developed for decomposing a mixture of particles into markers, and then, an edge-to-marker association method is proposed to identify the set of evidences that eventually delineate individual objects. We also provided theoretical justification regarding the separation capability of the first stage. In the second stage, the set of evidences become inputs to a Gaussian mixture model on B-splines, the solution of which leads to the joint learning of the missing contour and the particle shape. Using twelve real electron micrographs of overlapping nanoparticles, we compare the proposed method with seven state-of-the-art methods. The results show the superiority of the proposed method in terms of particle recognition rate.
dc.description.sponsorshipThe authors would like to acknowledge the generous support from their sponsors. Ding and Park are partially supported by US National Science Foundation (NSF) grants CMMI-0348150, CMMI-1000088, and Texas Norman Hackerman Advanced Research Program grant 010366-0024-2007; Huang is partially supported by NSF grants DMS-0907170, DMS-1007618, and King Abdullah University of Science and Technology award KUS-CI-016-04; Ji is partially supported by NSF grant 0748180. The authors would also like to thank Dr. Hong Liang and Dr. Subrata Kundu in the Department of Mechanical Engineering at Texas A&M University for providing the electron micrographs of gold nanoparticles.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.titleSegmentation, Inference and Classification of Partially Overlapping Nanoparticles
dc.typeArticle
dc.identifier.journalIEEE Transactions on Pattern Analysis and Machine Intelligence
dc.contributor.institutionDepartment of Industrial and Manufacturing Engineering, Florida A&M and Florida State University, Office: B319 COE, Tallahassee, FL 32310.
kaust.grant.numberKUS-CI-016-04


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