Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards automatic visual obstacle avoidance
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
ROVIS: robust machine vision for service robotic system FRIEND
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Visual content representation using semantically similar visual words
Expert Systems with Applications: An International Journal
Multi-view structure-from-motion for hybrid camera scenarios
Image and Vision Computing
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The SIFT algorithm (Scale Invariant Feature Transform) proposed by Lowe [1] is an approach for extracting distinctive invariant features from images. It has been successfully applied to a variety of computer vision problems based on feature matching including object recognition, pose estimation, image retrieval and many others. However, in real-world applications there is still a need for improvement of the algorithm's robustness with respect to the correct matching of SIFT features. In this paper, an improvement of the original SIFT algorithm providing more reliable feature matching for the purpose of object recognition is proposed. The main idea is to divide the features extracted from both the test and the model object image into several sub-collections before they are matched. The features are divided into several sub-collections considering the features arising from different octaves, that is from different frequency domains. To evaluate the performance of the proposed approach, it was applied to real images acquired with the stereo camera system of the rehabilitation robotic system FRIEND II. The experimental results show an increase in the number of correct features matched and, at the same time, a decrease in the number of outliers in comparison with the original SIFT algorithm. Compared with the original SIFT algorithm, a 40% reduction in processing time was achieved for the matching of the stereo images.