A Matrix-Based Approach to the Image Moment Problem
Journal of Mathematical Imaging and Vision
Discrete orthogonal moments in image analysis
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Fast and scalable computations of 2D image moments
Image and Vision Computing
Discrete orthogonal moments in image analysis
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
Intravascular ultrasound images vessel characterization using Adaboost
FIMH'03 Proceedings of the 2nd international conference on Functional imaging and modeling of the heart
A new set of normalized geometric moments based on Schlick's approximation
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
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Local moments have attracted attention as local features in applications such as edge detection and texture segmentation. The main reason for this is that they are inherently integral-based features, so that their use reduces the effect of uncorrelated noise. The computation of local moments, when viewed as a neighborhood operation, can be interpreted as a convolution of the image with a set of masks. Nevertheless, moments computed inside overlapping windows are not independent and convolution does not take this fact into account. By introducing a matrix formulation and the concept of accumulation moments, this paper presents an algorithm which is computationally much more efficient than convolving and yet as simple.