作者
Pedram Azad, Tamim Asfour, Rüdiger Dillmann
发表日期
2009/10/10
研讨会论文
2009 IEEE/RSJ international conference on intelligent robots and systems
页码范围
4275-4280
出版商
IEEE
简介
In the recent past, the recognition and localization of objects based on local point features has become a widely accepted and utilized method. Among the most popular features are currently the SIFT features, the more recent SURF features, and region-based features such as the MSER. For time-critical application of object recognition and localization systems operating on such features, the SIFT features are too slow (500-600 ms for images of size 640×480 on a 3 GHz CPU). The faster SURF achieve a computation time of 150-240 ms, which is still too slow for active tracking of objects or visual servoing applications. In this paper, we present a combination of the Harris corner detector and the SIFT descriptor, which computes features with a high repeatability and very good matching properties within approx. 20 ms. While just computing the SIFT descriptors for computed Harris interest points would lead to an …
引用总数
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Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition
P Azad, T Asfour, R Dillmann - 2009 IEEE/RSJ international conference on intelligent …, 2009