From Local Kernel to Nonlocal Multiple-Model Image Denoising
International Journal of Computer Vision
Properties of Savitzky--Golay digital differentiators
Digital Signal Processing
Myocardial motion and strain rate analysis from ultrasound sequences
IWCM'04 Proceedings of the 1st international conference on Complex motion
Fast motion detection based on accumulative optical flow and double background model
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Fast left ventricle tracking in 3D echocardiographic data using anatomical affine optical flow
FIMH'13 Proceedings of the 7th international conference on Functional Imaging and Modeling of the Heart
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We introduce local weighted geometric moments that are computed from an image within a sliding window at multiple scales. When the window function satisfies a two-scale relation, we prove that lower order moments can be computed efficiently at dyadic scales by using a multiresolution wavelet-like algorithm. We show that B-splines are well-suited window functions because, in addition to being refinable, they are positive, symmetric, separable, and very nearly isotropic (Gaussian shape). We present three applications of these multiscale local moments. The first is a feature-extraction method for detecting and characterizing elongated structures in images. The second is a noise-reduction method which can be viewed as a multiscale extension of Savitzky-Golay filtering. The third is a multiscale optical-flow algorithm that uses a local affine model for the motion field, extending the Lucas-Kanade optical-flow method. The results obtained in all cases are promising.