5537969
Individualizing Anatomical Atlases of the Head
This paper presents diffeomorphic transformations of 3D anatomical image data in human brains as imaged via magnetic resonance imagery. These transformations are generated in a hierarchical manner accommodating both global and local anatomical detail. The initial low-dimensional registration is accomplished by constraining the transformation to be in a low-dimensional basis. The basis is defined by the Green's function of the elasticity operator placed at predefined locations in the anatomy and the eigenfunctions of the elasticity operator. The high-dimensional, large-deformation registration is accomplished by minimizing the mismatch between the template (atlas) and target image volumes constrained by solving the Navier Stokes fluid model on a vector field.
12989752
Asymptotic performance analysis of Bayesian target recognition
This article investigates the asymptotic performance of Bayesian target recognition algorithms using deformable-template representations. Rigid computer-aided design (CAD) models represent the underlying targets; low-dimensional matrix Lie-groups (rotation and translation) extend them to particular instances. Remote sensors observing the targets are modeled as projective transformations, converting three-dimensional scenes into random images. Bayesian target recognition corresponds to hypothesis selection in the presence of nuisance parameters; its performance is quantified as the Bayes' error. Analytical expressions for this error probability in small noise situations are derived, yielding asymptotic error rates for exponential error probability decay.
15180139
Bayesian Segmentation via Asymptotic Partition Functions
Asymptotic approximations to the partition function of Gaussian random fields are derived. Textures are characterized via Gaussian random fields induced by stochastic difference equations determined by finitely supported, stationary, linear difference operators, adjusted to be nonstationary at the boundaries. It is shown that as the scale of the underlying shape increases, the log-normalizer converges to the integral of the log-spectrum of the operator inducing the random field. Fitting the covariance of the fields amounts to fitting the parameters of the spectrum of the differential operator-induced random field model. Matrix analysis techniques are proposed for handling textures with variable orientation. Examples of texture parameters estimated from training data via asymptotic maximum-likelihood are shown. Isotropic models involving powers of the Laplacian and directional models involving partial derivative mixtures are explored. Parameters are estimated for mitochondria and actin-myocin complexes in electron micrographs and clutter in forward-looking infrared images. Deformable template models are used to infer the shape of mitochondria in electron micrographs, with the asymptotic approximation allowing easy recomputation of the partition function as inference proceeds.
6192829
Clutter invariant ATR
One of the central problems in automated target recognition is to accommodate the infinite variety of clutter in real military environments. The principle focus of our paper is on the construction of metric spaces where the metric measures the distance between objects of interest invariant to the infinite variety of clutter. Such metrics are formulated using second-order random field models. Our results indicate that this approach significantly improves detection/classification rates of targets in clutter.
7684635
Information-Theoretic Bounds on Target Recognition Performance Based on Degraded Image Data
This paper derives bounds on the performance of statistical object recognition systems, wherein an image of a target is observed by a remote sensor. Detection and recognition problems are modeled as composite hypothesis testing problems involving nuisance parameters. We develop information-theoretic performance bounds on target recognition based on statistical models for sensors and data, and examine conditions under which these bounds are tight. In particular, we examine the validity of asymptotic approximations to probability of error in such imaging problems. Problems involving Gaussian, Poisson, and multiplicative noise, and random pixel deletions are considered, as well as least-favorable Gaussian clutter. A sixth application involving compressed sensor image data is considered in some detail. This study provides a systematic and computationally attractive framework for analytically characterizing target recognition performance under complicated, non-Gaussian models and optimizing system parameters.
7980241
Segmenting magnetic resonance images via hierarchical mixture modelling
We present a statistically innovative as well as scientifically and practically relevant method for automatically segmenting magnetic resonance images using hierarchical mixture models. Our method is a general tool for automated cortical analysis which promises to contribute substantially to the science of neuropsychiatry. We demonstrate that our method has advantages over competing approaches on a magnetic resonance brain imagery segmentation task.
15423783
Group Actions, Homeomorphisms, and Matching: A General Framework
This paper constructs metrics on the space of images I defined as orbits under group actions G. The groups studied include the finite dimensional matrix groups and their products, as well as the infinite dimensional diffeomorphisms examined in Trouvé (1999, Quaterly of Applied Math.) and Dupuis et al. (1998). Quaterly of Applied Math. Left-invariant metrics are defined on the product G × I thus allowing the generation of transformations of the background geometry as well as the image values. Examples of the application of such metrics are presented for rigid object matching with and without signature variation, curves and volume matching, and structural generation in which image values are changed supporting notions such as tissue creation in carrying one image to another.
2078197
Collaborative Agents for C2 Teamwork Simulation
Existing team training software often requires that trainees be organized as physical teams and the members of the same team be trained at the same time. To demonstrate that team training software can be made more flexible, this paper presents an approach to incorporating software agents into the distributed command-and-control (C2) simulation software DDD, which supports only human players. Based on the multi-agent architecture CAST, the software agents are designed as a team to perform C2 tasks in the DDD. By associating the agents with the same basic capabilities as the DDD provides its human users, we illustrate how to specify the teamwork knowledge for the agent team, and how to make agents efficiently reason about the dynamic, partially observable environment. Different methods of communication and coordination among agents are also briefly described.
28355264
Minimum Diameter of Diregular Digraphs of Degree 2
Etude de certaines relations entre le nombre de sommets, degre et diametre de graphes finis diregulaires. Les trois problemes consideres sont N(d,k), K(u,d) et D(u, k) ou, autrement dit, trouver le nombre maximum possible de sommets etant donnes le degre d et le diametre k; trouver le diametre minimum possible etant donnes le nombre de sommets n et le degre d et trouver le degre minimum possible etant donnes le nombre de sommets n et le diametre k respectivement
44565534
On the sum number of wheels
A graph G=(V,E) is a sum graph if there exists a labelling of the vertices of G with distinct positive integers so that uv∈E if and only if the sum of the labels assigned to u and v is the label of a vertex of G. The sum number of a graph is the minimum number of isolated vertices that, together with the given graph, form a sum graph. Sum graph labellings of wheels have been examined previously by other investigators. The sum number of an n spoked wheel, σ(Wn),n⩾5, has been found to be n when n is odd and n2+2 when n is even; however the proofs of these results are incomplete. In this paper we provide a complete proof and affirm that these previous results are correct.
10226081
A MAC protocol to reduce sensor network energy consumption using a wakeup radio
For increasing the life of sensor networks, each node must conserve energy as much as possible. In this paper, we propose a protocol in which energy is conserved by amortizing the energy cost of communication over multiple packets. In addition, we allow sensors to control the amount of buffered packets since storage space is limited. To achieve this, a two-radio architecture is used which allows a sensor to "wakeup" a neighbor with a busy tone and send its packets for that destination. However, this process is expensive because all neighbors must awake and listen to the primary channel to determine who is the intended destination. Therefore, triggered wakeups on the primary channel are proposed to avoid using the more costly wakeup procedure. We present a protocol for efficiently determining how large the period for these wakeups should be such that energy consumption is reduced.