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政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 > Item 140.119/110587

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/110587

Title:	A Lightweight Feature Descriptor Using Directional Edge Maps
Authors:	廖文宏
Contributors:	資科系
Keywords:	directional edge maps ； local feature descriptor ； object detection ； robot vision
Date:	2014-10
Issue Date:	2017-06-29 09:45:59 (UTC+8)
Abstract:	The objective of this research is to design a lightweight object detection and recognition engine that requires less space, less power and smaller budget than its PC counterparts. Specifically, we develop novel feature extraction algorithms to take ad-vantage of fixed-point arithmetic. The newly defined descriptor, known as directional edge maps (DEM), can be computed using simple addition/subtraction operations. DEMs are employed as locally invariant features to represent objects of interest. When combined with a modified AdaBoost classifier, the system can be trained to detect and recognize objects of various types. The performance of the proposed descriptor in several object recognition problems are examined and compared in terms of accuracy and efficiency against local binary descriptors (LBP) and Haar-like features.
Relation:	Journal of the Chinese Society of Mechanical Engineers(中國機械工程學刊), 35(5), 413-418
Data Type:	article
Appears in Collections:	[資訊科學系] 期刊論文

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