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IEEE Computer Graphics and Applications, Volume 31, Number 1, January/February 2011
A Viewer-Centric Editor for 3-D Movies
Sanjeev Koppal, Lawrence Zitnick, Michael F.Cohen, SingBing BingKang, Bryan Ressler, Alex Colburn
A proposed mathematical framework is the basis for a viewer-centric digital editor for 3D movies that's driven by the audience's perception of the scene. The editing tool allows both shot planning and after-the-fact digital manipulation of the perceived scene shape.
Presents Cramer-Rao (CR) bounds on error covariance for 2D and 3D parametric shape estimation. The motivation for this paper is ECT image reconstruction and uptake estimation with side information corresponding to organ boundaries extracted from high resolution MRI or CT.
It is important to understand the fundamental limitations on boundary estimation error covariance so as to gauge the utility of such side information. The authors present asymptotic forms of the Fisher information matrix for estimating 2D and 3D boundaries under a B-spline polar shape parameterization.
They show that circular (2D) and spherical (3D) shapes are the easiest to estimate in the sense of yielding maximum Fisher information. They also study the worst case shapes under near circularity and near sphericity constraints. Finally, a simulation is presented to illustrate the tightness of the CR bound for a simple 3D shape estimator utilizing edge filtering.
In this paper a minimax methodology is presented for combining information from two imaging modalities having different intrinsic spatial resolutions. The focus application is emission computed tomography (ECT), a low-resolution modality for reconstruction of radionuclide tracer density, when supplemented by high-resolution anatomical boundary information extracted from a magnetic resonance image (MRI) of the same imaging volume.
The MRI boundary within the two-dimensional (2-D) slice of interest is parameterized by a closed planar curve. The Cramer–Rao (CR) lower bound is used to analyze estimation errors for different boundary shapes. Under a spatially inhomogeneous Gibbs field model for the tracer density a representation for the minimax MRI-enhanced tracer density estimator is obtained. It is shown that the estimator is asymptotically equivalent to a penalized maximum likelihood (PML) estimator with resolution selective Gibbs penalty.
Quantitative comparisons are presented using the iterative space alternating generalized expectation maximization (SAGE-EM) algorithm to implement the PML estimator with and without minimax weight averaging.
A method for incorporating anatomical MRI boundary side information into penalized maximum likelihood (PML) emission computed tomography (ECT) image reconstructions using a set of averaged Gibbs weights was proposed by Hero and Piramuthu (see Proc. of IEEE/EURASIP Workshop on Nonlinear Signal and Image Processing, 1997).
A quadratic penalty based on Gibbs weights was used to enforce smoothness constraints everywhere in the image except across the estimated boundary of the ROI.
In this methodology, a limiting form of the posterior distribution of the MRI boundary parameters was used to average the Gibbs weights obtained by Titus, Hero and Fessler (see IEEE Int. Conf. on Image Processing, vol.2, Laussane, 1996).
There is an improvement in performance over the method proposed by Titus et al., when the variance of boundary estimates from the MRI data becomes significant. Here, we present the empirical performance analysis of the proposed method of averaged Gibbs weights.
The authors previously presented a methodology for incorporating perfect extracted MRI anatomical boundary estimates to improve the performance of penalized likelihood (PL) emission computed tomography (ECT) image reconstruction and ECT tracer uptake estimation. This technique used a spatially variant quadratic Gibbs penalty which enforced smoothness everywhere in the ECT image except across the MRI-extracted boundary of the ROI.
When high quality estimates of the anatomical boundary are available and MRI and ECT images are perfectly registered, the performance of this Gibbs penalty method is very close to that attainable using perfect side information, i.e., an errorless anatomical boundary estimate. However when the variance of the MRI-extracted boundary estimate becomes significant this method performs poorly. Here we present a modified Gibbs penalty function which accounts for errors in side information based on an asymptotic min-max robustness approach.
The resulting penalty is implemented with a set of averaged Gibbs weights where the averaging is performed with respect to a limiting form of the min-max induced posterior distribution of the MRI boundary parameters. Examples are presented for tracer uptake estimation using the SAGE version of the EM algorithm and various parameterizations of the anatomical boundaries.
In  a methodology for incorporating extracted MRI anatomical boundary information into penalized likelihood (PL) ECT image reconstructions and tracer uptake estimation was proposed. This methodology used quadratic penalty based on Gibbs weights which enforced smoothness constraints everywhere in the image except across the MRI-extracted boundary of the ROI.
When high quality estimates of the anatomical boundary are available and MRI and ECT images are perfectly registered, the performance of this method was shown to be very close to that attainable using ideal side information, i.e. noiseless anatomical boundary estimates.
However when the variance of the MRI-extracted boundary estimates becomes significant this penalty function method performs poorly. We give a modified Gibbs penalty function implemented with a set of averaged Gibbs weights, where the averaging is performed with respect to a limiting form of the posterior distribution of the MRI boundary parameters.
Journal of Experimental Psychology: Learning, Memory and Cognition, Vol 23, No. 5, 1247-1260, 1997
An investigation into cued recall of multi-attribute stimuli
Geoff Ward, Elizabeth Churchill, Paul Musgrove
Memory performance for sequences of letters positioned in particular spatial locations in a 3 x 3 grid was examined by requiring participants to recall attributes of the target stimuli given 1 or 2 features of the stimuli as cues. Cuing asymmetry was observed between the serial-position curves of object and sequential-order information, and location and sequential-order information, when the stimuli were presented in both the same and different locations.
After correcting for response bias, this asymmetry was attenuated for the stimuli presented in different locations and was eliminated for the stimuli presented in the same location.
Contrary to the predictions of the fragmentation hypothesis (G. V. Jones, 1976), asymmetry was also observed between object and location information. The roles of spatial location and response bias are offered as explanations for previous contradictory claims for cuing symmetry between item and order information.
This paper is concerned with the task of incrementally acquiring and refining the knowledge and algorithms of a knowledge-based system in order to improve its performance over time. In particular, we present the design of DE-KART, a tool whose goal is to provide increasing levels of assistance in acquiring and refining indexing and retrieval knowledge for a knowledge-based retrieval system.
DE-KART starts with knowledge that has been entered manually, and increase its level of assistance in acquiring and refining that knowledge, both in terms of the increased level of automation in interacting with users, and in terms of the increased generality of the knowledge.
DE-KART is at the intersection of machine learning and knowledge acquisition: it is a first step towards a system which moves along a continuum from interactive knowledge acquisition to increasingly automated machine learning as it acquires more knowledge and experience.