In this paper, we present an affine invariant feature descriptor, which is based on the well known Scale Invariant Feature Transform algorithm. The descriptor is a weighted histogram of gradient orientations and invariant against scale, in-plane rotation, stretch and skew. To cover the geometrical distortions introduced by an affine image transformation, we create a suiting, affine transformed coordinate system and perform all mathematical calculations in it. Furthermore, we propose to adapt the shape of the described region in order to gain invariance against affine transformations. We use elliptical respectively quadrangular regions instead of circular or quadratic regions. Based on synthetically transformed images we evaluate the matching performance of our descriptor in the case of available perfect knowledge about the image transformation, as well as for employing estimated transformation matrices. The results demonstrate that the proposed descriptor is capable of describing affine transformed regions well, even in case of strong affine image distortions.
[1]
Cordelia Schmid,et al.
Scale & Affine Invariant Interest Point Detectors
,
2004,
International Journal of Computer Vision.
[2]
ORTHONORMAL DECOMPOSITION OF FRACTAL INTERPOLATING FUNCTIONS
,
2006
.
[3]
Guizhong Liu,et al.
An improvement to the SIFT descriptor for image representation and matching
,
2013,
Pattern Recognit. Lett..
[4]
Christopher Hunt,et al.
Notes on the OpenSURF Library
,
2009
.
[5]
Yan Ke,et al.
PCA-SIFT: a more distinctive representation for local image descriptors
,
2004,
Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[6]
Xiaochun Cao,et al.
MIFT: A Mirror Reflection Invariant Feature Descriptor
,
2009,
ACCV.
[7]
Jean-Michel Morel,et al.
ASIFT: A New Framework for Fully Affine Invariant Image Comparison
,
2009,
SIAM J. Imaging Sci..
[8]
G LoweDavid,et al.
Distinctive Image Features from Scale-Invariant Keypoints
,
2004
.