Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Palmprint Identification Algorithm Using Hu Invariant Moments and Otsu Binarization
Proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science
A new class of Zernike moments for computer vision applications
Information Sciences: an International Journal
Exact and Speedy Computation of Legendre Moments on Binary Images
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
Pattern Recognition
An automatic method for generating affine moment invariants
Pattern Recognition Letters
Efficient and accurate computation of geometric moments on gray-scale images
Pattern Recognition
Static Hand Gesture Recognition and its Application based on Support Vector Machines
SNPD '08 Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Review article: Aircraft recognition in infrared image using wavelet moment invariants
Image and Vision Computing
Image analysis by Tchebichef moments
IEEE Transactions on Image Processing
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
Some computational aspects of discrete orthonormal moments
IEEE Transactions on Image Processing
Image recognition by affine Tchebichef moment invariants
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Performance evaluation of moment-based watermarking methods: A review
Journal of Systems and Software
Generalized dual Hahn moment invariants
Pattern Recognition
Hi-index | 0.01 |
A novel set of moment invariants based on the Krawtchouk moments are introduced in this paper. These moment invariants are computed over a finite number of image intensity slices, extracted by applying an innovative image representation scheme, the image slice representation (ISR) method. Based on this technique an image is decomposed to a several non-overlapped intensity slices, which can be considered as binary slices of certain intensity. This image representation gives the advantage to accelerate the computation of image's moments since the image can be described in a number of homogenous rectangular blocks, which permits the simplification of the computation formulas. The moments computed over the extracted slices seem to be more efficient than the corresponding moments of the same order that describe the whole image, in recognizing the pattern under processing. The proposed moment invariants are exhaustively tested in several well known computer vision datasets, regarding their rotation, scaling and translation (RST) invariant recognition performance, by resulting to remarkable outcomes.