Illumination robust interest point detection

Most interest point detection algorithms are highly sensitive to illumination variations. This paper presents a method to find interest points robustly even under large non-uniform photometric changes. The method, which we call illumination robust feature extraction transform (IRFET), determines salient interest points in an image by calculating and analyzing contrast signatures. A contrast signature shows the response of an interest point detector with respect to a set of contrast stretching functions. The IRFET is generic and can be used with most interest point detectors. In this paper, we demonstrate that the IRFET improves the repeatability rate of the Harris corner detector significantly (by around 25% on average in the experiments).

[1]  Hans P. Moravec.  Obstacle avoidance and navigation in the real world by a seeing robot rover , 1980 .

[2]  Farzin Mokhtarian,et al.  Robust Image Corner Detection Through Curvature Scale Space , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  P. Kovesi.  Image Features from Phase Congruency Image Features from Phase Congruency , 1995 .

[4]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[5]  Steve Mann,et al.  Comparametric equations with practical applications in quantigraphic image processing , 2000, IEEE Trans. Image Process..

[6]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Flore Faille,et al.  A fast method to improve the stability of interest point detection under illumination changes , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[8]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Flore Faille.  Stable Interest Point Detection under Illumination Changes Using Colour Invariants , 2005, BMVC.

[10]  Jie Wei,et al.  Illumination-invariant image retrieval and video segmentation , 1999, Pattern Recognit..

[11]  Peter Kovesi,et al.  Image Features from Phase Congruency , 1995 .

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

[13]  S. M. Steve.  SUSAN - a new approach to low level image processing , 1997 .

[14]  Shree K. Nayar,et al.  Radiometric self calibration , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[15]  Peter Meer,et al.  Point matching under large image deformations and illumination changes , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  D. Burr,et al.  Mach bands are phase dependent , 1986, Nature.

[17]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[18]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).