MI-SIFT: mirror and inversion invariant generalization for SIFT descriptor
Proceedings of the ACM International Conference on Image and Video Retrieval
Bivariate feature localization for SIFT assuming a Gaussian feature shape
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
A face authentication scheme based on Affine-SIFT (ASIFT) and structural similarity (SSIM)
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
On Combining Sequence Alignment and Feature-Quantization for Sub-Image Searching
International Journal of Multimedia Data Engineering & Management
An affine invariant shape retrieval algorithm
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Laplacian affine sparse coding with tilt and orientation consistency for image classification
Journal of Visual Communication and Image Representation
Video stabilization using maximally stable extremal region features
Multimedia Tools and Applications
Scars collaborative telediagnosis platform using adaptive image flow
Integrated Computer-Aided Engineering
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A fully affine invariant image comparison method, Affine-SIFT (ASIFT) is introduced. While SIFT is fully invariant with respect to only four parameters namely zoom, rotation and translation, the new method treats the two left over parameters : the angles defining the camera axis orientation. Against any prognosis, simulating all views depending on these two parameters is feasible. The method permits to reliably identify features that have undergone very large affine distortions measured by a new parameter, the transition tilt. State-of-the-art methods hardly exceed transition tilts of 2 (SIFT), 2.5 (Harris-Affine and Hessian-Affine) and 10 (MSER). ASIFT can handle transition tilts up 36 and higher (see Fig. 1).