We discuss a robust method for quantifying change of multi-temporal remote sensing point data in the presence of affine registration errors. Three dimensional image processing algorithms can be used to extract and model an electronic module, consisting of a self-contained assembly of electronic components and circuitry, using an ultrasound scanning sensor. Mutual information (MI) is an effective measure of change. We propose a multi-resolution 3D fractal algorithm which is a novel extension to MI or regional mutual information (RMI). Our method is called fractal mutual information (FMI). This extension efficiently takes neighborhood fractal patterns of corresponding voxels (3D pixels) into account. The goal of this system is to quantify the change in a module due to tampering and provide a method for quantitative and qualitative change detection and analysis.
We introduce a novel application for biometric data analysis. This technology can be used as part of a unique and systematic approach designed to augment existing processing chains. Our system provides image quality control and analysis capabilities. We show how analysis and efficient visualization are used as part of an automated process. The goal of this system is to provide a unified platform for the analysis of biometric images that reduce manual effort and increase the likelihood of a match being brought to an examiner’s attention from either a manual or lights-out application. We discuss the functionality of FeatureSCOPE™ which provides an efficient tool for feature analysis and quality control of biometric extracted features. Biometric databases must be checked for accuracy for a large volume of data attributes. Our solution accelerates review of features by a factor of up to 100 times. Review of qualitative results and cost reduction is shown by using efficient parallel visual review for quality control. Our process automatically sorts and filters features for examination, and packs these into a condensed view. An analyst can then rapidly page through screens of features and flag and annotate outliers as necessary.
The correlation of information from disparate sources has long been an issue in data fusion research. Traditional
data fusion addresses the correlation of information from sources as diverse as single-purpose sensors to all-source
multi-media information. Information system vulnerability information is similar in its diversity of sources and
content, and in the desire to draw a meaningful conclusion, namely, the security posture of the system under
inspection. FuzzyFusion<sup>TM</sup>, A data fusion model that is being applied to the computer network operations domain is
presented. This model has been successfully prototyped in an applied research environment and represents a next
generation assurance tool for system and network security.