@inproceedings{ed7f836016e2477da6c1ec8e4a086596,
title = "Feature Reduction and Noise Removal in SURF Framework for Efficient Object Recognition in Images",
abstract = "Speeded up Robust Features (SURF) is an interest point detector and descriptor which has been popularly used for object recognition. However, in real time object recognition applications, SURF framework can not be used because of its expensive nature. In this paper, a feature reduction process is proposed by using only the most repeatable features for matching. The feature reduction step results in a remarkable computational speed up with little loss of accuracy. A noise-reduction process allows a further increase in matching speed and also reduces the false positive rates. A modified definition of the second-neighbor in the nearest neighbor ratio matching strategy allows matching with increased reliability. The comparative analysis with SURF framework shows that the proposed framework can be useful in applications where the accuracy can be sacrificed to save computational cost.",
keywords = "Object recognition, SURF features, Pose estimation, Noise reduction, Feature matching",
author = "N. Ejaz and R. Baik and S.W. Baik",
year = "2012",
month = dec,
day = "11",
doi = "10.1007/978-94-007-5860-5\_63",
language = "English",
isbn = "978-94-007-5859-9",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer, Dordrecht",
pages = "529--535",
booktitle = "IT Convergence and Security 2012",
}