Affine shape adaptation of blobs moving in 3D space

The paper studies new constraints that characterize a 3D-motion field as observed from the relative motion of a camera. Such constraints are derived from the relative change in size of observed local image regions over time. To consider the image distortions that arise in a projective camera, a modified affine shape adaptation scheme is proposed for the case of blob detection, with an emphasis on robustness under important viewpoint changes and changes in lighting conditions. The resulting features and constraints are used to characterize the motion of an ego-vehicle by means of their navigation angles. We present results on synthetic as well as on real-world image sequences.

[1]  Krystian Mikolajczyk,et al.  Detection of local features invariant to affines transformations , 2002 .

[2]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[3]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[4]  R. Deriche.  Recursively Implementing the Gaussian and its Derivatives , 1993 .

[5]  Lucas J. van Vliet,et al.  Recursive implementation of the Gaussian filter , 1995, Signal Process..

[6]  Tony Lindeberg,et al.  Scale-Space Theory in Computer Vision , 1993, Lecture Notes in Computer Science.

[7]  Tony Lindeberg,et al.  Shape-Adapted Smoothing in Estimation of 3-D Depth Cues from Affine Distortions of Local 2-D Brightness Structure , 1994, ECCV.

[8]  Tony Lindeberg,et al.  Direct computation of shape cues using scale-adapted spatial derivative operators , 1996, International Journal of Computer Vision.

[9]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[10]  Adam Baumberg,et al.  Reliable feature matching across widely separated views , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[11]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[12]  K. Mikolajczyk.  Detection of local features invariant to affine transformations : application to matching and recognition , 2002 .

[13]  J. Koenderink.  The structure of images , 2004, Biological Cybernetics.

[14]  Tony Lindeberg,et al.  A scale selection principle for estimating image deformations , 1998, Image Vis. Comput..

[15]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[16]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[17]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[18]  Bernd Jähne,et al.  Differential Range Flow Estimation , 1999, DAGM-Symposium.

[19]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[20]  Reinhard Klette.  Shape from area and centroid , 1995 .