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15
Simultaneous detection of lane and pavement boundaries using modelbased multisensor fusion
 IEEE Transactions on Intelligent Transportation Systems
, 2000
"... Abstract—This paper treats a problem arising in the design of intelligent vehicles: automated detection of lane and pavement boundaries using forwardlooking optical and radar imaging sensors mounted on an automobile. In previous work, lane and pavement boundaries have always been located separately ..."
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Cited by 34 (5 self)
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Abstract—This paper treats a problem arising in the design of intelligent vehicles: automated detection of lane and pavement boundaries using forwardlooking optical and radar imaging sensors mounted on an automobile. In previous work, lane and pavement boundaries have always been located separately. This separate detection strategy is problematic in situations when either the optical or the radar image is too noisy. In this paper, we propose a Bayesian multisensor image fusion method to solve our boundary detection problem. This method makes use of a deformable template model to globally describe the boundaries of interest. The optical and radar imaging processes are described with random field likelihoods. The multisensor fusion boundary detection problem is reformulated as a joint MAP estimation problem. However, the joint MAP estimate is intractable, as it involves the computation of a notoriously difficult normalization constant, also known as the partition function. Therefore, we settle for the socalled empirical MAP estimate, as an approximation to the true MAP estimate. Several experimental results are provided to demonstrate the efficacy of the empirical MAP estimation method in simultaneously detecting lane and pavement boundaries. Fusion of multimodal images is not only of interest to the intelligent vehicles community, but to others as well, such as biomedicine, remote sensing, target recognition. The method presented in this paper is also applicable to image fusion problems in these other areas. I.
Quantitative spectroscopic diffuse optical tomography guided by imperfect a priori structural information,” Phys
 Med. Biol
, 2005
"... Abstract. Spectroscopic Diffuse Optical Tomography (DOT) can directly image the concentrations of physiologically significant chromophores in the body. This information may be of importance in characterizing breast tumors and distinguishing them from benign structures. This paper studies the accurac ..."
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Cited by 10 (3 self)
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Abstract. Spectroscopic Diffuse Optical Tomography (DOT) can directly image the concentrations of physiologically significant chromophores in the body. This information may be of importance in characterizing breast tumors and distinguishing them from benign structures. This paper studies the accuracy with which lesions can be characterized given a physiologically realistic situation in which the background architecture of the breast is heterogeneous yet highly structured. Specifically, in simulation studies, we assume that the breast is segmented into distinct glandular and adipose regions. Imaging with a highresolution imaging modality, such as Magnetic Resonance Imaging, in conjunction with a segmentation by a clinical expert, allows the glandular/adipose boundary to be determined. We then apply a twostep approach in which the background chromophore concentrations of each region are estimated in a nonlinear fashion, and a more localized lesion is subsequently estimated using a linear perturbational approach. In addition, we examine the consequences which errors in the breast segmentation have on estimating both the background and inhomogeneity chromophore concentrations. § To whom correspondence should be addressed (gboverma@ece.neu.edu) 1.
On achievable accuracy for rangefinder localization
 Proceedings of the IEEE International Conference on Robotics and Automation (ICRA
"... Abstract — The covariance of every unbiased estimator is bounded by the Cramér–Rao lower bound, which is the inverse of Fisher’s information matrix. This paper shows that, for the case of localization with rangefinders, Fisher’s matrix is a function of the expected readings and of the orientation o ..."
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Abstract — The covariance of every unbiased estimator is bounded by the Cramér–Rao lower bound, which is the inverse of Fisher’s information matrix. This paper shows that, for the case of localization with rangefinders, Fisher’s matrix is a function of the expected readings and of the orientation of the environment’s surfaces at the sensed points. The matrix also offers a mathematically sound way to characterize underconstrained situations as those for which it is singular: in those cases the kernel describes the direction of maximum uncertainty. This paper also introduces a simple model of unstructured environments for which the Cramér–Rao bound is a function of two statistics of the shape of the environment: the average radius and a measure of the irregularity of the surfaces. Although this model is not valid for all environments, it allows for some interesting qualitative considerations. As an experimental validation, this paper reports simulations comparing the bound with the actual performance of the ICP (Iterative Closest/Corresponding Point) algorithm. Finally, it is discussed the difficulty in extending these results to find a lower bound for accuracy in scan matching and SLAM. I.
Iterative methods for image reconstruction
 in ISBI Tutorial. 2006, http://www.eecs.umich.edu/ fessler/papers/files/talk/08/isbinotes.pdf
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Recovering convex edges of an image from noisy tomographic data
 IEEE Transactions on Information Theory
, 2006
"... Abstract — We consider the problem of recovering edges of an image from noisy tomographic data. The original image is assumed to have a discontinuity jump (edge) along the boundary of a compact convex set. The Radon transform of the image is observed with noise, and the problem is to estimate the ed ..."
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Cited by 4 (0 self)
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Abstract — We consider the problem of recovering edges of an image from noisy tomographic data. The original image is assumed to have a discontinuity jump (edge) along the boundary of a compact convex set. The Radon transform of the image is observed with noise, and the problem is to estimate the edge. We develop an estimation procedure which is based on recovering support function of the edge. It is shown that the proposed estimator is nearly optimal in order in a minimax sense. Numerical examples illustrate reasonable practical behavior of the estimation procedure. Index Terms — Radon transform, optimal rates of convergence, support function, edge detection, minimax estimation I.
CramerRao bounds for nonparametric surface reconstruction from range data
, 2003
"... The CramerRao error bound provides a fundamental limit on the expected performance of a statistical estimator. The error bound depends on the general properties of the system, but not on the specific properties of the estimator or the solution. The CramerRao error bound has been applied to scalar ..."
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The CramerRao error bound provides a fundamental limit on the expected performance of a statistical estimator. The error bound depends on the general properties of the system, but not on the specific properties of the estimator or the solution. The CramerRao error bound has been applied to scalar and vectorvalued estimators and recently to parametric shape estimators. However, nonparametric, lowlevel surface representations are an important tool in 3D reconstruction, and are particularly useful for representing complex scenes with arbitrary shapes and topologies. This paper presents a generalization of the CramerRao error bound to nonparametric shape estimators. Specifically, we derive the error bound for the full 3D reconstruction of scenes from multiple range images. 1.
Estimation and Statistical Bounds for ThreeDimensional Polar Shapes in Diffuse Optical Tomography
"... Abstract—Voxelbased reconstructions in diffuse optical tomography (DOT) using a quadratic regularization functional tend to produce very smooth images due to the attenuation of high spatial frequencies. This then causes difficulty in estimating the spatial extent and contrast of anomalous regions ..."
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Abstract—Voxelbased reconstructions in diffuse optical tomography (DOT) using a quadratic regularization functional tend to produce very smooth images due to the attenuation of high spatial frequencies. This then causes difficulty in estimating the spatial extent and contrast of anomalous regions such as tumors. Given an assumption that the target image is piecewise constant, we can employ a parametric model to estimate the boundaries and contrast of an inhomogeneity directly. In this paper, we describe a method to directly reconstruct such a shape boundary from diffuse optical measurements. We parameterized the object boundary using a spherical harmonic basis, and derived a method to compute sensitivities of measurements with respect to shape parameters. We introduced a centroid constraint to ensure uniqueness of the combined shape/center parameter estimate, and a projected Newton method was utilized to optimize the object center position
This is Al Hero’s and Jeff Fessler’s section to the DARPA MOSAIC proposal. 1 Algorithms for Control and Image Reconstruction
"... The algorithm development and analysis team, led by Profs. Fessler and Hero, will be involved in many aspects of instrument control, image processing, and image analysis. In particular we will use suitable mathematical models to predict imaging performance, develop subsurface reconstruction algorith ..."
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The algorithm development and analysis team, led by Profs. Fessler and Hero, will be involved in many aspects of instrument control, image processing, and image analysis. In particular we will use suitable mathematical models to predict imaging performance, develop subsurface reconstruction algorithms, register multiple position scans, correct for probearray crosstalk, and develop feedback algorithms for adaptive control of the probetip array. In the description below we focus on algorithms applicable to an array of thermal probe tips whosepositions are individually controllable. If time permits, we will also explore control and image reconstruction issues for: 1) piezoelectric probe tips; and 2) tandemtip probes having tips mounted on a single rigidcontrollable platform. Such a system would be a cheaper and mechanically simpler design which would allow for more densely packed tips having the potential of higher spatial resolution. The height of the platform would be controlled to maintain a constant average force over all tips, resulting in a measurement from which the individually controlled array measurements would be demultiplexed from the measurements using signal processing. 1.1 Active Probe Control Adaptive feedback control will be crucial for maximizing accuracy of the proposed constantforce multipleprobe AFM system. The adaptive modelreference control framework described below will lead to great improvements in image resolution by feeding back partially extracted information about the sample surface and subsurface structures
Statistical methods for image reconstruction
, 2002
"... ... on statistical image reconstruction methods. The purpose of the annotation is to provide supplemental details, and particularly to provide extensive literature references for further study. ..."
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... on statistical image reconstruction methods. The purpose of the annotation is to provide supplemental details, and particularly to provide extensive literature references for further study.