Fast cortical keypoints for real-time object recognition

Best-performing object recognition algorithms employ a large number features extracted on a dense grid, so they are too slow for real-time and active vision. In this paper we present a fast cortical keypoint detector for extracting meaningful points from images. It is competitive with state-of-the-a...

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Bibliographic Details
Main Authors: Terzic, Kasim, Rodrigues, J. M. F., du Buf, J. M. H.
Format: Conference Proceeding
Language:English
Subjects:
Computer vision
Gabor filters
Image classification
Object recognition
Real time systems
Online Access:Request full text
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Summary:Best-performing object recognition algorithms employ a large number features extracted on a dense grid, so they are too slow for real-time and active vision. In this paper we present a fast cortical keypoint detector for extracting meaningful points from images. It is competitive with state-of-the-art detectors and particularly well-suited for tasks such as object recognition. We show that by using these points we can achieve state-of-the-art categorization results in a fraction of the time required by competing algorithms.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2013.6738695