The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affine/ Photometric Invariants for Planar Intensity Patterns
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
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
Hybrid entity clustering using crowds and data
The VLDB Journal — The International Journal on Very Large Data Bases
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The SIFT (Scale Invariant Feature Transform) descriptor is a widely used method for matching image features. However, perfect scale invariance can not be achieved in practice because of sampling artefacts, noise in the image data, and the fact that the computational effort limits the number of analyzed scale space images. In this paper we propose a modification of the descriptor's regular grid of orientation histogram bins to an irregular grid. The irregular grid approach reduces the negative effect of scale error and significantly increases the matching precision for image features. Results with a standard data set are presented that show that the irregular grid approach outperforms the original SIFT descriptor and other state-of-the-art extentions.