Color-Based Moment Invariants for Viewpoint and Illumination Independent Recognition of Planar Color Patterns

This paper contributes to the viewpoint and illumination independent recognition of planar color patterns such as labels, logos, signs, pictograms, etc. by means of moment invariants. It introduces the idea of using powers of the intensities in the different color bands of a color image and combinations thereof for the construction of the moments. First, a complete classification is made of all functions of such moments which are invariant under both affine deformations of the pattern (thus achieving viewpoint invariance) as well as linear changes of the intensity values in the individual color bands (hence, coping with changes in the irradiance pattern due to different lighting conditions and/or viewpoints). The discriminant power and classification performance of these new invariants for color pattern recogniti on has been tested on a data set consisting of images of real outdoors advertising panels. Furthermore, a comparison to moment invariants presented in literature ([1] and [2]) that come closest sto the aimed type of invariants is made and new approaches to improve their performance are presented.

[1]  S. Maitra Moment invariants , 1979, Proceedings of the IEEE.

[2]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[3]  Oliver R. Hinton,et al.  About Moment Normalization and Complex Moment Descriptors , 1988, Pattern Recognition.

[4]  Andrew Zisserman,et al.  Geometric invariance in computer vision , 1992 .

[5]  Richard J. Prokop,et al.  A survey of moment-based techniques for unoccluded object representation and recognition , 1992, CVGIP Graph. Model. Image Process..

[6]  Andrew Zisserman,et al.  Applications of Invariance in Computer Vision , 1993, Lecture Notes in Computer Science.

[7]  Joseph L. Mundy,et al.  Repeated Structures: Image Correspondence Constraints and 3D Structure Recovery , 1993, Applications of Invariance in Computer Vision.

[8]  Jan Flusser,et al.  Pattern recognition by affine moment invariants , 1993, Pattern Recognit..

[9]  Thomas H. Reiss,et al.  Recognizing Planar Objects Using Invariant Image Features , 1993, Lecture Notes in Computer Science.

[10]  Glenn Healey,et al.  Using illumination invariant descriptors for recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Shree K. Nayar,et al.  Seeing Beyond Lambert's Law , 1994, ECCV.

[12]  Lawrence B. Wolff,et al.  On the relative brightness of specular and diffuse reflection , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[13]  G. Healey,et al.  Using Illumination Invariant Color Histogram Descriptors for Recognit ion , 1994 .

[14]  Hanns Schulz-Mirbach,et al.  Anwendung von Invarianzprinzipien zur Merkmalgewinnung in der Mustererkennung , 1995 .

[15]  Arnold W. M. Smeulders,et al.  A comparative study of several color models for color image invariant retrieval , 1996 .

[16]  Luc Van Gool,et al.  Affine/ Photometric Invariants for Planar Intensity Patterns , 1996, ECCV.

[17]  Glenn Healey,et al.  Using Zernike moments for the illumination and geometry invariant classification of multispectral texture , 1998, IEEE Trans. Image Process..