Image Features Based on a New Approach to 2D Rotation Invariant Quadrature Filters

Quadrature filters are a well known method of low-level computer vision for estimating certain properties of the signal, as there are local amplitude and local phase. However, 2D quadrature filters suffer from being not rotation invariant. Furthermore, they do not allow to detect truly 2D features as corners and junctions unless they are combined to form the structure tensor. The present paper deals with a new 2D generalization of quadrature filters which is rotation invariant and allows to analyze intrinsically 2D signals. Hence, the new approach can be considered as the union of properties of quadrature filters and of the structure tensor. The proposed method first estimates the local orientation of the signal which is then used for steering some basis filter responses. Certain linear combination of these filter responses are derived which allow to estimate the local isotropy and two perpendicular phases of the signal. The phase model is based on the assumption of an angular band-limitation in the signal. As an application, a simple and efficient point-of-interest operator is presented and it is compared to the Plessey detector.

[1]  Keith Baker,et al.  5th Alvey vision Conference , 1990, Image Vis. Comput..

[2]  Michael Felsberg,et al.  The monogenic signal , 2001, IEEE Trans. Signal Process..

[3]  Michael Felsberg,et al.  Scale Adaptive Filtering Derived from the Laplace Equation , 2001, DAGM-Symposium.

[4]  J. Bigun,et al.  Optimal Orientation Detection of Linear Symmetry , 1987, ICCV 1987.

[5]  Gerhard Krieger,et al.  Nonlinear image operators for the evaluation of local intrinsic dimensionality , 1996, IEEE Trans. Image Process..

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

[7]  Hans Knutsson,et al.  Signal processing for computer vision , 1994 .

[8]  E. Stein,et al.  Introduction to Fourier Analysis on Euclidean Spaces. , 1971 .

[9]  Joachim Weickert,et al.  Recursive Separable Schemes for Nonlinear Diffusion Filters , 1997, Scale-Space.

[10]  Joachim Weickert,et al.  A Review of Nonlinear Diffusion Filtering , 1997, Scale-Space.

[11]  Thomas Bülow,et al.  Hypercomplex signals-a novel extension of the analytic signal to the multidimensional case , 2001, IEEE Trans. Signal Process..

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

[13]  Michael Felsberg,et al.  A New Extension of Linear Signal Processing for Estimating Local Properties and Detecting Features , 2000, DAGM-Symposium.

[14]  A.V. Oppenheim,et al.  The importance of phase in signals , 1980, Proceedings of the IEEE.

[15]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

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

[18]  S. Hahn.  Hilbert Transforms in Signal Processing , 1996 .

[19]  Jan J. Koenderink,et al.  What is a "Feature"? , 1993 .

[20]  S. Sangwine.  Fourier transforms of colour images using quaternion or hypercomplex, numbers , 1996 .

[21]  SuomelaRiku,et al.  Robust Image Corner Detection Through Curvature Scale Space , 1998 .