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dc.contributor.authorTham, Jie Shengen_US
dc.contributor.authorChen, Yong-Shenen_US
dc.contributor.authorFauzi, Mohammad Faizal Ahmaden_US
dc.contributor.authorChang, Yoong Choonen_US
dc.date.accessioned2017-04-21T06:49:22Z-
dc.date.available2017-04-21T06:49:22Z-
dc.date.issued2016en_US
dc.identifier.isbn978-1-5090-2073-7en_US
dc.identifier.urihttp://hdl.handle.net/11536/134558-
dc.description.abstractMoment invariants have been widely used in various applications in image pattern recognition research field due to its invariant features of translation, scaling and rotation. In this paper, a computer vision based ambient assisted living technique for drinking activity assistance by using moment invariant to recognize the depth image object was developed. The extracted depth image objects dataset contains different type of mugs with different views. We employed the invariant features on the extracted depth image objects to recognize and classify the objects into different categories. Experimental result shows that higher accuracy can be achieve with the proposed technique compared to existing methods on depth image objects recognition.en_US
dc.language.isoen_USen_US
dc.titleDepth Image Object Recognition using Moment Invariantsen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2016 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN (ICCE-TW)en_US
dc.citation.spage25en_US
dc.citation.epage26en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000392265400013en_US
dc.citation.woscount0en_US
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