Show simple item record

dc.contributor.authorZhao, Haifeng
dc.contributor.authorZhou, Jun
dc.contributor.authorGu, Yanyang
dc.contributor.authorHo, Chee Meng Benjamin
dc.contributor.authorTan, Say Hwa
dc.contributor.authorGao, Yongsheng
dc.date.accessioned2019-06-10T01:32:19Z
dc.date.available2019-06-10T01:32:19Z
dc.date.issued2018
dc.identifier.isbn9781538668689
dc.identifier.doi10.1109/RCAR.2018.8621816
dc.identifier.urihttp://hdl.handle.net/10072/384019
dc.description.abstractIn this paper, we propose a novel framework to achieve real-time droplet detection and recognition. In a fluidics system, water drops can be generated to carry objects. It is important to use such droplets to analyze the properties of the carried tiny objects. In real applications, the movement of droplets are captured by a high-speed camera, and processed by an image-based computing system. This imposes high requirements on the computational efficiency of the image processing and pattern recognition methods in the system. To tackle this challenge, we propose to use efficient object tracking and classification methods to build the framework. We first use the Hough transform as the basic droplet detection method, and then explore the relationship of the same droplet in different frames to track their movement. For the classification step, we use the area of particle content as the basic feature, and conduct classification using a linear support vector machine. The experiments on real-time droplet analysis system verifies that our proposed framework can process images at 1000 frames per second, while achieving 99.3% in classification accuracy.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.relation.ispartofconferencenameIEEE International Conference on Real-time Computing and Robotics (IEEE RCAR)
dc.relation.ispartofconferencetitlePROCEEDINGS OF 2018 IEEE INTERNATIONAL CONFERENCE ON REAL-TIME COMPUTING AND ROBOTICS (IEEE RCAR)
dc.relation.ispartofdatefrom2018-08-01
dc.relation.ispartofdateto2018-08-05
dc.relation.ispartoflocationKandima, MALDIVES
dc.relation.ispartofpagefrom589
dc.relation.ispartofpagefrom6 pages
dc.relation.ispartofpageto594
dc.relation.ispartofpageto6 pages
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleReal-Time Computing for Droplet Detection and Recognition
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorZhou, Jun
gro.griffith.authorGao, Yongsheng
gro.griffith.authorTan, Say Hwa H.


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record