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.

title: Segmenting magnetic resonance images via hierarchical mixture modelling
abstract: 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.

title: Rate-distortion theory applied to automatic object recognition
abstract: We consider the problem of recognizing CAD models at arbitrary orientations observed via the projective transformation on an imaging sensor with noise. Bounds on codebook size are established through the rate-distortion curve for a distortion measure derived from the Hilbert-Schmidt norm for elements of the orthogonal group.

title: On The Geometry and Shape of Brain Sub-Manifolds
abstract: This paper develops mathematical representations for neuro-anatomically significant substructures of the brain and their variability in a population. The focus of the paper is on the neuro-anatomical variation of the geometry and the "shape" of two-dimensional surfaces in the brain. As examples, we focus on the cortical and hippocampal surfaces in an ensemble of Macaque monkeys and human MRI brains. The "shapes" of the substructures are quantified via the construction of templates; the variations are represented by defining probabilistic deformations of the template. Methods for empirically estimating probability measures on these deformations are developed by representing the deformations as Gaussian random vector fields on the embedded sub-manifolds. The Gaussian random vector fields are constructed as quadratic mean limits using complete orthonormal bases on the sub-manifolds. The complete orthonormal bases are generated using modes of vibrations of the geometries of the brain sub-manifolds. The covari...

title: A stochastic model for studying the laminar structure of cortex from MRI
abstract: The human cerebral cortex is a laminar structure about 3 mm thick, and is easily visualized with current magnetic resonance (MR) technology. The thickness of the cortex varies locally by region, and is likely to be influenced by such factors as development, disease and aging. Thus, accurate measurements of local cortical thickness are likely to be of interest to other researchers. We develop a parametric stochastic model relating the laminar structure of local regions of the cerebral cortex to MR image data. Parameters of the model include local thickness, and statistics describing white, gray and cerebrospinal fluid (CSF) image intensity values as a function of the normal distance from the center of a voxel to a local coordinate system anchored at the gray/white matter interface. Our fundamental data object, the intensity-distance histogram (IDH), is a two-dimensional (2-D) generalization of the conventional 1-D image intensity histogram, which indexes voxels not only by their intensity value, but also by their normal distance to the gray/white interface. We model the IDH empirically as a marked Poisson process with marking process a Gaussian random field model of image intensity indexed against normal distance. In this paper, we relate the parameters of the IDH model to the local geometry of the cortex. A maximum-likelihood framework estimates the parameters of the model from the data. Here, we show estimates of these parameters for 10 volumes in the posterior cingulate, and 6 volumes in the anterior and posterior banks of the central sulcus. The accuracy of the estimates is quantified via Cramer-Rao bounds. We believe that this relatively crude model can be extended in a straightforward fashion to other biologically and theoretically interesting problems such as segmentation, surface area estimation, and estimating the thickness distribution in a variety of biologically relevant contexts.

title: Information measures for object recognition accommodating signature variability
abstract: This paper presents measures characterizing the information content of remote observations of ground scenes imaged via optical and infrared sensors. Object recognition is posed in the context of deformable templates; the special Euclidean group is used to accommodate geometric variation of object pose. Principal component analysis of object signatures is used to represent and efficiently accommodate variation in object signature due to changes in the thermal state of the object surface. Mutual information measures, which are independent of the recognition system, are calculated quantifying both the information gain due to remote observations of the scene and the information loss due to signature variability. Signature model mismatch is quantitatively examined using the Kullback-Leibler divergence. Expressions are derived quadratically approximating the posterior conditional entropy on the orthogonal group for high signal-to-noise ratio. It is demonstrated that quadratic modules accurately characterize the posterior entropy for broad ranges of signal-to-mise ratio. Information gain in multiple-sensor scenarios is quantified, and it is demonstrated that the cost of signature uncertainty for the Comanche series of FLIR images collected by the US Army Night Vision Electronic Sensors Directorate is approximately 0.8 bits with an associated near doubling of mean-squared error uncertainty in pose.

title: Leveraging Channel Diversity for Key Establishment in Wireless Sensor Networks
abstract: As the use of sensor networks increases, security in this domain becomes a very real concern. One fundamental aspect of providing confidentiality and authentication is key distribution. While public-key encryption has provided these properties historically, sensors are resource constrained and benefit from symmetric key approaches. In this work, we propose a novel protocol for symmetric key distribution that uses the multiple channels available on sensor hardware. This channel diversity, along with spatial diversity of device locations, allows neighboring sensors to establish secure link keys from plaintext keys that are broadcast by sensors in their neighborhood. In particular, we show that using even one extra channel during the initialization procedure significantly improves the security of key establishment. Via analysis and simulation, we show that our protocol performs well in terms of network connectivity and resilience to colluding malicious devices, when compared to previous work. We show that our protocol can achieve over 90% connectivity among neighboring sensors with link keys that are uncompromised even when 80% of the devices in the network are malicious and collude. Finally, we present a thorough discussion of the comparative advantages and disadvantages of our approach compared to previous techniques.

title: Proving Facts about "|"
abstract: We study the Knights and Knaves problem, and find that for a proper treatment via theorem-proving, an interaction with natural language processing research is helpful. In particular, we discuss Ohlbach's claim that first-order logic is not well suited to handling this problem. Then we provide another interpretation of the problem using indexicals, and axiomatize it so that the desired result follows. We conclude by suggesting a broader context for dealing with "self-utterances" in automatic theorem-proving. Fuller details of automated proofs are given in a longer paper.

title: Generalized Domination in Chordal Graphs
abstract: We discuss the computational complexity of generalized domination problems, which were introduced in  Complexity of domination-type problems in graphs, Nordic Journal of Computing 1 (1994), 157-171], restricted to chordal and interval graphs. The existence problem, parametrized by two sets of nonnegative integers σ and ρ, asks for the existence of a set S of vertices of a given graph such that for every vertex u ∈ S (or u ∉ S), the number of neighbors of u which are in S is in σ (in ρ, respectively). Telle proved that this problem is NP-complete for general graphs, provided both σ and ρ are finite and 0 ∉ ρ. One of our main results shows that in such cases, the existence problem is polynomially solvable for interval graphs. On the other hand, for chordal graphs, the complexity of the existence problem varies significantly even when σ and ρ contain one element each.

title: Bounds on the number of isolates in sum graph labeling
abstract: A simple undirected graph H is called a sum graph if there is a labeling L of the vertices of H into distinct positive integers such that any two vertices u and v of H are adjacent if and only if there is a vertex w with label L(w )= L(u )+ L(v). The sum number � (G) of a graph G =( V; E) is the least integer r such that the graph H consisting of G and r isolated vertices is a sum graph. It is clear that � (G)6|E|. In this paper, we discuss general upper and lower bounds on the sum number. In particular, we prove that, over all graphs G =( V; E) with 5xed |V |?3 and |E|, the average of � (G) is at least |E |− 3|V |(log|V |)=[log(( |V | 2 )=|E|)] −| V |− 1. In other words, for most graphs, � (G) ∈ (|E|).

title: Complete characterization of almost Moore digraphs of degree three
abstract: It is well known that Moore digraphs do not exist except for trivial cases (degree 1 or diameter 1), but there are digraphs of diameter two and arbitrary degree which miss the Moore bound by one. No 2004 Wiley Periodicals, Inc.