On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Index-based object recognition in pictorial data management
Computer Vision, Graphics, and Image Processing
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parts of Visual Form: Computational Aspects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
A distance and angle similarity measure method
Journal of the American Society for Information Science
Computer Vision
Modal Matching for Correspondence and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complete Sets of Complex Zernike Moment Invariants and the Role of the Pseudoinvariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the Sixth International Conference on Data Engineering
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Local Moment Invariant Analysis
CGIV '05 Proceedings of the International Conference on Computer Graphics, Imaging and Visualization
The PicToSeek WWW Image Search System
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Shape retrieval based on dynamic programming
IEEE Transactions on Image Processing
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
Unsupervised extraction of visual attention objects in color images
IEEE Transactions on Circuits and Systems for Video Technology
A systematic method for efficient computation of full and subsets Zernike moments
Information Sciences: an International Journal
Fast and numerically stable methods for the computation of Zernike moments
Pattern Recognition
Content-based emblem retrieval using Zernike moments
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Face recognition using Zernike and complex Zernike moment features
Pattern Recognition and Image Analysis
Classification of benign and malignant masses based on Zernike moments
Computers in Biology and Medicine
Fast shape re-ranking with neighborhood induced similarity measure
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Space-time Zernike moments and pyramid kernel descriptors for action classification
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Invariant curvature-based Fourier shape descriptors
Journal of Visual Communication and Image Representation
Accurate calculation of Zernike moments
Information Sciences: an International Journal
Information Sciences: an International Journal
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Shape is a fundamental image feature used in content-based image-retrieval systems. This paper proposes a robust and effective shape feature, which is based on a set of orthogonal complex moments of images known as Zernike moments (ZMs). As the rotation of an image has an impact on the ZM phase coefficients of the image, existing proposals normally use magnitude-only ZM as the image feature. In this paper, we compare, by using a mathematical form of analysis, the amount of visual information captured by ZM phase and the amount captured by ZM magnitude. This analysis shows that the ZM phase captures significant information for image reconstruction. We therefore propose combining both the magnitude and phase coefficients to form a new shape descriptor, referred to as invariant ZM descriptor (IZMD). The scale and translation invariance of IZMD could be obtained by prenormalizing the image using the geometrical moments. To make the phase invariant to rotation, we perform a phase correction while extracting the IZMD features. Experiment results show that the proposed shape feature is, in general, robust to changes caused by image shape rotation, translation, and/or scaling. The proposed IZMD feature also outperforms the commonly used magnitude-only ZMD in terms of noise robustness and object discriminability.