A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical analysis with missing data
Statistical analysis with missing data
A Frequency Domain Algorithm for Multiframe Detection and Estimation of Dim Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computation of component image velocity from local phase information
International Journal of Computer Vision
Hypercomplex spectral transformations
Hypercomplex spectral transformations
Image-flow computation: an estimation-theoretic framework and a unified perspective
CVGIP: Image Understanding
Performance of optical flow techniques
International Journal of Computer Vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Techniques and standards for image, video, and audio coding
Techniques and standards for image, video, and audio coding
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Processing and Communications
Video Processing and Communications
Motion segmentation and pose recognition with motion history gradients
Machine Vision and Applications - Special issue: IEEE WACV
Generalized image matching by the method of differences
Generalized image matching by the method of differences
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
Joint optical flow estimation, segmentation, and 3D interpretation with level sets
Computer Vision and Image Understanding
Narrow directional steerable filters in motion estimation
Computer Vision and Image Understanding
An FFT-based technique for translation, rotation, and scale-invariant image registration
IEEE Transactions on Image Processing
Spatiotemporal approach for time-varying global image motion estimation
IEEE Transactions on Image Processing
A harmonic retrieval framework for discontinuous motion estimation
IEEE Transactions on Image Processing
Extension of phase correlation to subpixel registration
IEEE Transactions on Image Processing
Estimation of Multiple Accelerated Motions Using Chirp-Fourier Transform and Clustering
IEEE Transactions on Image Processing
Hypercomplex Fourier Transforms of Color Images
IEEE Transactions on Image Processing
Representing moving images with layers
IEEE Transactions on Image Processing
Fast hypercomplex polar fourier analysis for image processing
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
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The problem of estimating the motions in image sequences has been extensively studied. However, most of the proposed motion estimation approaches deal only with the luminance component of the images, although the color information is also important. The usual way to apply these techniques in color image sequences is to process each color channel separately. As proposed by Ell, Sangwine and Moxey, a more sophisticated approach is to handle the color channels in a ''holistic'' manner, using quaternions. We present a motion estimation approach which is based on the Hypercomplex (quaternionic) Phase Correlation of Moxey et al. We show that the quaternionic approach is computationally more efficient and more robust to sensor noise, compared with the corresponding three-channels-separately approach. With the assumption of smoothly time-varying velocities we propose the application of a weighted fuzzy c-means clustering procedure to the obtained velocity estimates. This renders the estimation more robust. A methodology in the hypercomplex Fourier transform domain for separating the moving objects/layers is also presented. An energy-minimization-based approach for the spatial assignment of the velocities and the creation of segmentation maps, is given. We furthermore show how to apply the motion estimation approach locally in space for the extraction of dense motion fields. Our experimental results and comparisons with state-of-the-art methodologies verify the effectiveness of the proposed approach.