Uncertainty in the registration between two images remains a problematic source of error in performing change detection between them. While a number of methods have been developed for reducing the impact of registration error in change detection, none of these methods are based upon a statistical characterization of the uncertainty in the estimate of the registration transformation. When utilizing a feature-point based registration algorithm, we can compute a Cramer-Rao lower bound (CRLB) on the estimate of the registration transformation based on an assumed covariance in the feature-point locations. This information can be used to predict the variance on the location at which pixels will appear in the registered image, which can be used to estimate the bias and variance introduced into the pixel intensities by registration uncertainty. Here, we use this information to improve change detection performance and verify this improvement with simulated and experimental results.
Based on the fundamental scattering mechanisms of facetized computer-aided design (CAD) models, we are able to define expected contributions (EC) to the radar signature. The net result of this analysis is the prediction of the salient aspects and contributing vehicle morphology based on the aspect. Although this approach does not provide the fidelity of an asymptotic electromagnetic (EM) simulation, it does provide very fast estimates of the unique scattering that can be consumed by a signature exploitation algorithm. The speed of this approach is particularly relevant when considering the high dimensionality of target configuration variability due to articulating parts which are computationally burdensome to predict. The key scattering phenomena considered in this work are the specular response from a single bounce interaction with surfaces and dihedral response formed between the ground plane and vehicle. Results of this analysis are demonstrated for a set of civilian target models.
Vibration signatures sensed from distant vehicles using laser vibrometry systems provide valuable information that may be used to help identify key vehicle features such as engine type, engine speed, and number of cylinders. Through the use of physics models of the vibration phenomenology, features are chosen to support classification algorithms. Various individual exploitation algorithms were developed using these models to classify vibration signatures into engine type (piston vs. turbine), engine configuration (Inline 4 vs. Inline 6 vs. V6 vs. V8 vs. V12) and vehicle type. The results of these algorithms will be presented for an 8 class problem. Finally, the benefits of using a factor graph representation to link these independent algorithms together will be presented which constructs a classification hierarchy for the vibration exploitation problem.
Using multi-frame change detection methods, we estimate which pixels include objects that are in motion relative to
the background. We utilize both a sequential statistical change detection method and a sparsity-based change detection
method. We perform foreground estimation in videos in which the background is static as well as in images in which
apparent background motion is induced by camera motion. We show the results of our techniques on the background
subtraction data set from the Statistical Visual Computing Lab at the University of California, San Diego(UCSD).
This paper presents an algorithm for detecting handguns in terahertz images. Terahertz radiation is capable of penetrating
certain materials which are opaque at optical wavelengths, such as clothing, without the harmful effects of
ionizing radiation. The approach taken is to segment objects of interest and classify them based on shape. We use a
modified version of an active contour algorithm found in the open literature. Modifications include: a pre-processing
step that includes clutter filtering and seeding of an initial contour; and a post-processing step that removes clutter
pixels from the segmentation. The features used for classification are moment-based and Fourier shape descriptors.
Classification as handgun or non-handgun from these features is done via Fisher's linear discriminant.