James Coggins

Page 1. Recruitment of Helminth Parasites by Bluegills (Lepomis macrochirus) Using a Modified Live-Box Technique • GERALD W. Esca Department of Biology Wake Forest University Winston-Salem, North Carolina 27109 GEORGE C. CAMPBELL RUSSELL E. CONNERS IF. ...

Page 1. Recruitment of Helminth Parasites by Bluegills (Lepomis macrochirus) Using a Modified Live-Box Technique • GERALD W. Esca Department of Biology Wake Forest University Winston-Salem, North Carolina 27109 GEORGE C. CAMPBELL RUSSELL E. CONNERS IF. ...

Inhomogeneities in the fields of magnetic resonance (MR) systems cause the statistical characteristics of tissue classes to vary within the resulting MR images. These inhomogeneities must be taken into consideration when designing an algorithm for automated tissue classification. The traditional approach in image processing would be to apply a gain field correction technique to remove the inhomogeneities from the images. Statistical solutions would most likely focus on including spatial information in the feature space of the classifier so that it can be trained to model and adjust for the inhomogeneities. This paper will prove that neither of these general approaches offer a complete and viable solution. This paper will prove that neither of these general approaches offers a complete and viable solution. This paper will in fact show that not only do the inhomogeneities modify the local mean and variance of a tissue class as is commonly accepted, but the inhomogeneities also induce a rotation of the covariance matrices. As a result, gain field correction techniques cannot compensate for all of the artifacts associated with inhomogeneities. Additionally, it will be demonstrated that while statistical methods can capture all of the anomalies, the across patient and across time variations of the inhomogeneities necessitate frequent and time consuming retraining of any Bayesian classifier. This paper introduces a two stage process for MR tissue classification which addresses both of these issues by utilizing techniques from both image processing and statistics. First, a band-pass mean field corrector is used to alleviate the mean and variance deformations in each image. Then, using a kernel mixture model classifier couple to an interactive data augmentation tool, the user can selectively refine and explore the class representations for localized regions of the image and thereby capture the rotation of the covariance matrices. This approach is shown to outperform Gaussian classifiers and 4D mixture modeling techniques when both the final accuracy and user time requirements are considered.

This study is an initial investigation into the efficacy of texture operators for detection of military vehicle targets-in-the-clear in SAR imagery. The specific study is a very simple problem that aims to evaluate a particular feature set that arises in an approach to computer vision called spatial spectroscopy. Spatial spectroscopy begins by partitioning the image's spatial (Fourier) spectrum using a bank of filters. The filters compute a multiscale, truncated Taylor Series expansion at each pixel. Suitably extended on generic images, this feature space is capable of producing a unique pattern describing each pixel. The objective, of course, is not to uniquely distinguish each pixel but to form groups of pixels corresponding to targets in SAR that are distinct from background pixels. Thus, nonlinear operators are required to fold, twist, and bend the feature space in ways that cause pixels that make up targets to group together. The particular nonlinear operators for a study depend on the invariances and equivariances of the problem. In the present case, a large suite of operators is applied to the image data and principal discriminant analysis is used to select the most relevant features. Texture operators are found to be effective at discriminating targets from background.

ABSTRACT Curvilinear targets are common in many imaging modalities. Detection of such targets can be challenging because of their multiscale structure, their frequent obscuration in natural imagery, their turns, intersections, and merges, and the prevalence of false positive detections based on local information. Using a spatial spectroscopy approach, we introduce image analysis methods that use the concept of gauge frames to simplify the identification of curvilinear targets. Fast computational approximation methods are described for gauge fields, and an experiment is described illustrating the power of higher-order derivatives for understanding even relatively simple geometric structures. Methods for extracting coherent curvilinear objects that exploit the larger-scale commonalities of points in the object are described.

... cool o DTIC S ELECTE S JUN 1 1990 L D James Coggins SoftLab Software Systems Laboratory The University of North Carolina Department of Computer Science CB#3175, Sitterson Hall Chapel Hill, NC 27599-3175 ... Authors James Coggins Page 3. The COOL Library ...

... you decide to forward this column to the Philosophy Department for interpreta-tion, let's begin with a seemingly straightforward inquiry from Scott Meyers 65 ... Anders Juul Munch provides a test for determining the type of'0'and then peels away a layer of the onion: Try: printf (8% d ...

... I didn't have a very good answer then. Fred, I would like hereby to amend that response. Page 5. 10/18/89 ... This idea is applied in several sublibraries of COOL. An enhancement to this library that is in the works is the use of the XDR protocols to read and write disk files. ...

A novel method for representing image orientation structure is used to measure the orientations of line segments in a series of increasingly blurred images. An algorithm for mapping filtered image data into an orientation feature space is defined. The algorithm is applied using four sets of filters. The results show that the algorithm effectively exploits redundancy in the feature values to yield robust inferences across a broad range of scales and through large amounts of blurring

thispaper, the training data consists of hand labeledpixels from three coronal slice images. Figures 1and 2 are one of the 57 slices used for testing. Thisdata was collected during a single scanning run ona 1.5 Tesla GE MRI system at the University ofIowa. MR parameters were chosen to provide thebest visual separation of the classes (echo time -- 32and 96

This study is an initial investigation into the efficacy of texture operators for detection of military vehicle targets-in-the-clear in SAR imagery. The specific study is a very simple problem that aims to evaluate a particular feature set that arises in an approach to computer vision called spatial spectroscopy. Spatial spectroscopy begins by partitioning the image's spatial (Fourier) spectrum using a bank of filters. The filters compute a multiscale, truncated Taylor Series expansion at each pixel. Suitably extended on generic images, this feature space is capable of producing a unique pattern describing each pixel. The objective, of course, is not to uniquely distinguish each pixel but to form groups of pixels corresponding to targets in SAR that are distinct from background pixels. Thus, nonlinear operators are required to fold, twist, and bend the feature space in ways that cause pixels that make up targets to group together. The particular nonlinear operators for a study d...

Multiscale geometric image structure analysis is used to produce a hierarchical labeling of image regions. The regions provide a language for fast, interactive object definition. The approach allows human analysts to quickly inject semantics into the image representation, enhancing rather than trying to replace the human operator's capabilities.

ABSTRACT Linear methods are strongly preferred in statistical pattern recognition, but problems in perception require nonlinear analysis and operators. Even the most successful linear methods lack robustness, especially when the normal variation in the data reveals new structure. An alternative to computing complex features or devising a complex decision rule is to transform the feature space so that the structure of the density is simplified. Simple nonlinear operations such as folding, applying gauge coordinate transformations, and nonlinear diffusion have been explored. The ultimate objective is to derive the appropriate nonlinear transformations from training data or from a verbal description of the classification task in terms of the variances, equivariances, and invariances of the problem

... you decide to forward this column to the Philosophy Department for interpreta-tion, let's begin with a seemingly straightforward inquiry from Scott Meyers 65 ... Anders Juul Munch provides a test for determining the type of'0'and then peels away a layer of the onion: Try: printf (8% d ...

Michael A. Bajura:Merging Real and Virtual EnvironmentswithVideo See-Through Head-Mounted Displays(Under the direction of Henry Fuchs)This dissertation explores the problem of inserting virtual (computer-generated) objects into naturalscenes in video-based augmented-reality (AR) systems. The augmented-reality system problemsaddressed are the shorter-term goal of making synthetic objects appear to be more stable and registeredand the longer-term goal of presenting proper occlusion and...