Simple Gabor feature space for invariant object recognition

Invariant object recognition is one of the most challenging problems in computer vision. The authors propose a simple Gabor feature space, which has been successfully applied to applications, e.g., in invariant face detection to extract facial features in demanding environments. In the proposed feature space, illumination, rotation, scale, and translation invariant recognition of objects can be realized within a reasonable amount of computation. In this study, fundamental properties of Gabor features, construction of the simple feature space, and invariant search operations in the feature space are discussed in more detail.

[1]  Yehoshua Y. Zeevi,et al.  The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[3]  Hyun Seung Yang,et al.  Invariant object detection based on evidence accumulation and Gabor features , 2001, Pattern Recognit. Lett..

[4]  Dennis Gabor,et al.  Theory of communication , 1946 .

[5]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[6]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[7]  Ville Kyrki.  Local and Global Feature Extraction for Invariant Object Recognition , 2002 .

[8]  Edward H. Adelson,et al.  Shiftable multiscale transforms , 1992, IEEE Trans. Inf. Theory.

[9]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Gerald Sommer,et al.  Gabor wavelet networks for efficient head pose estimation , 2002, Image Vis. Comput..

[11]  M. Bastiaans,et al.  Gabor's expansion of a signal into Gaussian elementary signals , 1980, Proceedings of the IEEE.

[12]  Joni-Kristian Kämäräinen,et al.  Robustness of Gabor Feature Parameter Selection , 2002, MVA.

[13]  J.-K. Kamarainen,et al.  Noise tolerant object recognition using Gabor filtering , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[14]  Joni-Kristian Kämäräinen,et al.  Fundamental frequency Gabor filters for object recognition , 2002, Object recognition supported by user interaction for service robots.

[15]  G. Granlund.  In search of a general picture processing operator , 1978 .

[16]  Josef Kittler,et al.  Hypotheses-Driven Affine Invariant Localization of Faces in Verification Systems , 2003, AVBPA.

[17]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

[18]  Josef Kittler,et al.  Invariant Gabor Features for Face Evidence Extraction , 2002, MVA.

[19]  Heikki Kälviäinen,et al.  Content-Based Image Matching Using Gabor Filtering , 2001 .