@inproceedings {335,
	title = {An iterative approach to improved local phase coherence estimation},
	booktitle = {4th Annual Canadian Conference on Computer and Robot Vision},
	year = {2008},
	abstract = {<p>This paper introduces a novel iterative approach to estimating local phase coherence in situations characterized by low signal-to-noise ratios. Local phase coherence is used for a wide range of computer vision applications such as edge and corner detection and object description. An issue faced in extracting local phase coherence is the presence of image noise. While existing approaches to dealing with noise when estimating local phase coherence is effective for low noise situations, they are inadequate for situations contaminated by high levels of noise. In the proposed approach, the issue of high image noise is addressed by re-estimating both the local phase coherence and the underlying image content iteratively to improve local phase coherence estimates. This is performed using a feedback loop model, where the local phase coherence estimates are used to re-estimate the image content using a moment-adaptive bilateral estimation scheme and information from the re-estimated image content is used to re-estimate the local phase coherence. Experiment results show that the proposed approach can be used to provide improved local phase coherence estimates.</p>
},
	keywords = {Computer Vision, corner detection, edge detection, high image noise, iterative approach, iterative methods, local phase coherence estimation, low signal-to-noise ratios, moment-adaptive bilateral estimation scheme, object description},
	doi = {http://dx.doi.org/10.1109/CRV.2008.18},
	author = {A Wong}
}
@article {587,
	title = {Mapping and pseudo-inverse algorithms for data assimilation},
	journal = {IEEE International Geoscience and Remote Sensing Symposium},
	volume = {6},
	year = {2002},
	pages = {3221 - 3223},
	abstract = {<p>Among existing ocean data assimilation methodologies, reduced-state Kalman filters are a widely-studied compromise between resolution, optimality, error specification, and computational feasibility. In such reduced-state filters, the measurement update takes place on a coarser grid than that of the general circulation model (GCM); therefore, these filters require mapping operators from the GCM grid to the reduced state and vice-versa. The general requirements are that the state-reduction and interpolation operators be pseudo-inverses of each other, that the coarse state defines a closed dynamical system, that the mapping operations be insensitive to noise, and that they be appropriate for regions with irregular coastlines and bathymetry. In this paper we investigate a variety of approaches, including computing the pseudoinverse by brute force, using the FFT, subsampling methods, implicit methods, and finally develop a novel iterative approach. We also evaluate the mapping performance of eleven interpolation kernels; surprisingly, common kernels such as bilinear, exponential, Gaussian, and sinc, performed only moderately well. This comprehensive study greatly reduces the computational bottleneck and guesswork of pseudo-inverse algorithms, making possible the application of reduced-state filters to global problems at state-of-the-art resolution.</p>
},
	keywords = {bathymetry, closed dynamical system, coarse state, computational bottleneck, data assimilation, FFT, GCM grid, general circulation model, geophysical signal processing, implicit methods, interpolation, interpolation operators, irregular coastlines, iterative approach, Kalman filters, mapping algorithms, ocean data assimilation methodologies, oceanographic techniques, pseudo-inverse algorithms, reduced order systems, reduced-state filters, reduced-state Kalman filters, state-reduction, subsampling methods},
	doi = {http://dx.doi.org/10.1109/IGARSS.2002.1027136},
	author = {P Fieguth and D Menemenlis and I Fukumori}
}
