Understanding eyewitness lineup judgment processes is critical, both from a theoretical standpoint (to better understand human memory) and from a practical one (to prevent wrongful convictions and criminals walking free). Currently, two influential theories attempt to explain lineup decision making: the theory of eyewitness judgment strategies (Lindsay & Wells, 1985), and the signal detection theory-informed diagnostic-feature-detection hypothesis (Wixted & Mickes, 2014). The theory of eyewitness judgment strategies posits that eyewitnesses can adopt either an absolute judgment strategy (base identification decisions only on their memory for the culprit) or a relative judgment strategy (base identification decision on lineup member comparisons). This theory further predicts that relative judgment strategies lead to an increase in false identifications. Contrast this with the diagnostic-feature-detection hypothesis, which predicts that the lineup member comparisons inherent to relative strategies promote greater accuracy. These two theories have been tested indirectly (i.e., via lineup format manipulations tangentially related to the theory), but there is a lack of direct tests. Across two experiments (Ns = 192, 584), we presented participants with simulated crime videos and corresponding lineups, and manipulated judgment strategy using explicit absolute and relative strategy instructions and a novel rank-order manipulation meant to encourage lineup member comparisons. We found no substantial differences in identifications or overall accuracy as a function of instructed strategy. These results are inconsistent with the theory of eyewitness judgment strategies but provide some support for the diagnostic-feature-detection hypothesis. We discuss implications for both theories and future lineup research. / Graduate

Indiana University-Purdue University Indianapolis (IUPUI) / A Multigraph is a set of graphs with a common set of nodes but different sets of edges. Multigraph visualization has not received much attention so far. In this thesis, I will introduce an interactive application in brain network data analysis that has a strong need for multigraph visualization. For this application, multigraph was used to represent brain connectome networks of multiple human subjects. A volumetric data set was constructed from the matrix representation of the multigraph. A volume visualization tool was then developed to assist the user to interactively and iteratively detect network features that may contribute to certain neurological conditions. I applied this technique to a brain connectome dataset for feature detection in the classification of Alzheimer's Disease (AD) patients. Preliminary results showed significant improvements when interactively selected features are used.

Typically, computational fluid dynamic (CFD) solutions produce large amounts of data that can be used for analysis. The enormous amount of data produces new challenges for effective exploration. The prototype system EVITA, based on ranked access of application-specific regions of interest, provides an effective tool for this purpose. Automated feature detection techniques are needed to identify the features in the dataset. Automated techniques for detecting shocks, expansion regions, vortices, separation lines, and attachment lines have already been developed. A new approach for identifying the regions of flow separation is proposed. This technique assumes that each pair of separation and attachment lines has a vortex core associated with it. It is based on the velocity field in the plane perpendicular to the vortex core. The present work describes these methods along with the results obtained.

Master of Science / Department of Electrical and Computer Engineering / Steven Warren / Pulse oximetry, a noninvasive circulatory system monitoring technique, has been widely adopted in clinical and homecare applications for the determination of heart rate and blood oxygen saturation, where measurement locations are typically limited to fingertips and earlobes. Prior research indicates a variety of additional clinical parameters that can be derived from a photoplethysmogram (PPG), the fundamental time-domain signal yielded by a pulse oximeter sensor. The gap between this research potential and practical device applications can be decreased by improvements in device design (e.g., sensor performance and geometry, sampling fidelity and reliability, etc.) and PPG signal processing. This thesis documents research focused on a novel pulse oximeter design and the accompanying PPG signal processing and interpretation. The filter-free reflectance design adopted in the module supplements new methods for signal sampling, control, and processing, with a goal to acquire high-fidelity raw data that can provide additional physiologic data for state-of-health analyses. Effective approaches are also employed to improve signal stability and quality, including shift-resistant baseline control, an anti-aliasing sampling frequency, light emitting diode intensity autoregulation, signal saturation inhibition, etc. MATLAB interfaces provide data visualization and processing for multiple applications. A feature detection algorithm (decision-making rule set) is presented as the latest application, which brings the element of intelligence into the pulse oximeter design by enabling onboard signal quality verification. Two versions of the reflectance sensor were designed, built, calibrated, and utilized in data acquisition work. Raw data, which are composed of four channels of signals at a 240 Hz sampling rate and a 12-bit precision, successfully stream to a personal computer via a serial connection or wireless link. Due to the optimized large-area sensor and the intensity autoregulation mechanism, PPG signal acquisition from measurement sites other than fingertips and earlobes, e.g., the wrist, become viable and retain signal quality, e.g., signal-to-noise ratio. With appropriate thresholds, the feature detection algorithm can successfully indicate motion occurrence, signal saturation, and signal quality level. Overall, the experimental results from a variety of subjects and body locations in multiple applications demonstrate high quality PPGs, prototype reliability, and prospects for further research value.

The purpose of this thesis is to study and investigate a practical and efficient implementation of corner orientation detection using multisteerable filters. First, practical theory involved in applying multisteerable filters for corner orientation estimation is presented. Methods to improve the efficiency with which multisteerable corner filters are applied to images are investigated and presented. Prior research in this area presented an optimization equation for determining the best match of corner orientations in images; however, little research has been done on optimization techniques to solve this equation. Optimization techniques to find the maximum response of a similarity function to determine how similar a corner feature is to a multioriented corner template are also explored and compared in this research.

The problem of automatic face recognition is to visually identify a person in an input image. This task is performed by matching the input face against the faces of known people in a database of faces. Most existing work in face recognition has limited the scope of the problem, however, by dealing primarily with frontal views, neutral expressions, and fixed lighting conditions. To help generalize existing face recognition systems, we look at the problem of recognizing faces under a range of viewpoints. In particular, we consider two cases of this problem: (i) many example views are available of each person, and (ii) only one view is available per person, perhaps a driver's license or passport photograph. Ideally, we would like to address these two cases using a simple view-based approach, where a person is represented in the database by using a number of views on the viewing sphere. While the view-based approach is consistent with case (i), for case (ii) we need to augment the single real view of each person with synthetic views from other viewpoints, views we call 'virtual views'. Virtual views are generated using prior knowledge of face rotation, knowledge that is 'learned' from images of prototype faces. This prior knowledge is used to effectively rotate in depth the single real view available of each person. In this thesis, I present the view-based face recognizer, techniques for synthesizing virtual views, and experimental results using real and virtual views in the recognizer.

In this master thesis a visual odometry system is implemented and explained. Visual odometry is a technique, which could be used on autonomous vehicles to determine its current position and is preferably used indoors when GPS is notworking. The only input to the system are the images from a stereo camera and the output is the current location given in relative position. In the C++ implementation, image features are found and matched between the stereo images and the previous stereo pair, which gives a range of 150-250 verified feature matchings. The image coordinates are triangulated into a 3D-point cloud. The distance between two subsequent point clouds is minimized with respect to rigid transformations, which gives the motion described with six parameters, three for the translation and three for the rotation. Noise in the image coordinates gives reconstruction errors which makes the motion estimation very sensitive. The results from six experiments show that the weakness of the system is the ability to distinguish rotations from translations. However, if the system has additional knowledge of how it is moving, the minimization can be done with only three parameters and the system can estimate its position with less than 5 % error.

This thesis is concerned with development of a terrain model using Gaussian Processes to support the automation of open-pit mines. Information can be provided from a variety of sources including GPS, laser scans and manual surveys. The information is then fused together into a single representation of the terrain together with a measure of uncertainty of the estimated model. The model is also used to detect and label specific features in the terrain. In the context of mining, theses features are edges known as toes and crests. A combination of clustering and classification using supervised learning detects and labels these regions. Data gathered from production iron ore mines in Western Australia and a farm in Marulan outside Sydney is used to demonstrate and verify the ability of Gaussian Processes to estimate a model of the terrain. The estimated terrain model is then used for detecting features of interest.Results show that the Gaussian Process correctly estimates the terrain and uncertainties, and provide a good representation of the area. Toes and crests are also successfully identified and labelled.

Today, 3D models of cities are created from aerial images using a camera rig. Images, together with sensor data from the flights, are stored for further processing when building 3D models. However, there is a market demand for a more mobile solution of satisfactory quality. If the camera position can be calculated for each image, there is an existing algorithm available for the creation of 3D models. This master thesis project aims to investigate whether the iPhone 4 offers good enough image and sensor data quality from which 3D models can be created. Calculations on movements and rotations from sensor data forms the foundation of the image processing, and should refine the camera position estimations. The 3D models are built only from image processing since sensor data cannot be used due to poor data accuracy. Because of that, the scaling of the 3D models are unknown and a measurement is needed on the real objects to make scaling possible. Compared to a test algorithm that calculates 3D models from only images, already available at the SBD’s system, the quality of the 3D model in this master thesis project is almost the same or, in some respects, even better when compared with the human eye.

A fundamental problem in spacecraft proximity operations is the determination of the 6 degree of freedom relative navigation solution between the observer reference frame and a reference frame tied to a proximal body. For the most unconstrained case, the proximal body may be uncontrolled, and the observer spacecraft has no a priori information on the body. A spacecraft in this scenario must simultaneously map the generally poorly known body being observed, and safely navigate relative to it. Simultaneous localization and mapping(SLAM)is a difficult problem which has been the focus of research in recent years. The most promising approaches extract local features in 2D or 3D measurements and track them in subsequent observations by means of matching a descriptor. These methods exist for both active sensors such as Light Detection and Ranging(LIDAR) or laser RADAR(LADAR), and passive sensors such as CCD and CMOS camera systems. This dissertation presents a method for fusing time of flight(ToF) range data inherent to scanning LIDAR systems with the passive light field measurements of optical systems, extracting features which exploit information from each sensor, and solving the unique SLAM problem inherent to spacecraft proximity operations. Scale Space analysis is extended to unstructured 3D point clouds by means of an approximation to the Laplace Beltrami operator which computes the scale space on a manifold embedded in 3D object space using Gaussian convolutions based on a geodesic distance weighting. The construction of the scale space is shown to be equivalent to both the application of the diffusion equation to the surface data, as well as the surface evolution process which results from mean curvature flow. Geometric features are localized in regions of high spatial curvature or large diffusion displacements at multiple scales. The extracted interest points are associated with a local multi-field descriptor constructed from measured data in the object space. Defining features in object space instead of image space is shown to bean important step making the simultaneous consideration of co-registered texture and the associated geometry possible. These descriptors known as Multi-Field Diffusion Flow Signatures encode the shape, and multi-texture information of local neighborhoods in textured range data. Multi-Field Diffusion Flow Signatures display utility in difficult space scenarios including high contrast and saturating lighting conditions, bland and repeating textures, as well as non-Lambertian surfaces. The effectiveness and utility of Multi-Field Multi-Scale(MFMS) Features described by Multi-Field Diffusion Flow Signatures is evaluated using real data from proximity operation experiments performed at the Land Air and Space Robotics(LASR) Laboratory at Texas A&M University.