Color-invariant shape moments for object recognition

Qiang Zhou; Mehmet Celenk

doi:10.1117/12.424971

8 May 2001 Color-invariant shape moments for object recognition

Qiang Zhou, Mehmet Celenk

Proceedings Volume 4304, Nonlinear Image Processing and Pattern Analysis XII; (2001) https://doi.org/10.1117/12.424971
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

Abstract

Geometric moments have been widely used in many shape recognition and object classification tasks. These monomials are usually computed from binary or gray-level images for the object shape recognition invariant to rotation, translation, and scaling. In this paper, we attempt to calculate the shape related moments from color images, and study their noise immunity and color invariance property for the application areas of face recognition and content based image retrieval. To this end, we describe a computationally efficient method of converting a vector-valued color image into a gray scale for robust moment computation. Geometric moments are calculated from the resultant scalar representation of a color image data, and proven to be robust shape descriptors for the face and flower images. The generated shape invariants appear to have better noise immunity than the Hu moments and exhibit characteristics invariant to hue changes in the object colors. As compared to the Zernike polynomials, the proposed feature set has higher discriminatory power although the Zernike polynomials present superior noise rejection capability. Robust performance, computational efficiency, high noise immunity, and hue invariance property of the new approach are particularly useful for fast image retrieval tasks requiring high query accuracy.

Citation Download Citation

Qiang Zhou and Mehmet Celenk "Color-invariant shape moments for object recognition", Proc. SPIE 4304, Nonlinear Image Processing and Pattern Analysis XII, (8 May 2001); https://doi.org/10.1117/12.424971

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available