KEYWORDS: LIDAR, Matrices, 3D image processing, Image segmentation, 3D modeling, 3D acquisition, Principal component analysis, Visualization, Data modeling, Vector spaces
Manifold extraction techniques, such as ISOMAP, are capable of projecting nonlinear, high-dimensional data to a lower-dimensional
subspace while retaining discriminatory information. In this investigation, ISOMAP is applied to 3D
LADAR range imagery. Selected man-made objects are reduced to sets of spin-image feature vectors that describe object
surface geometries. At various spin-image support scales, we use the distribution-free Henze-Penrose statistic test to
quantify differences between man-made objects in both the high-dimensional spin-image vector representation and in the
low-dimensional spin-image manifold extracted using ISOMAP.
Extraction and efficient representation of informative structure from data is the goal of pattern recognition. Efficient and effective parametric and nonparametric representations for capturing the geometry of three-dimensional objects are an area of current research. Tang and Medioni have proposed tensor representations for characterization and reconstruction of surfaces. 3-D structure tensors are extracted by mapping surface geometries using a rank-2 covariant tensor. Distributional differences between representations of objects of interest can (theoretically) be used for target matching and identification. This paper analyzes the statistical distributions of tensor representation extracted from 3-D LADAR imagery and quantifies a measure of divergence between images of three vehicles as a function of tensor feature support size.
Raytheon has developed a new tactical form-factored, imaging LADAR (LAser Detection And Ranging) seeker. In a joint activity with AMRDEC, the seeker was used in a tower test data collection at the Russell Measurement Facility at Redstone Arsenal, Alabama. The seeker collected 3D imagery of fixed structures and vehicles embedded in various clutter backgrounds for use in analysis of computer vision and automatic target recognition techniques. This paper presents a high-level overview of the seeker, a description of the test activities, representative LADAR range and intensity imagery collected during the test, and 3D rendered scenes constructed from the imagery.
Spin images originated within the robotics group at Carnegie Mellon University and are representations of 3-space surface regions. This representation provides a means for surface matching that is invariant to rigid body rotations and translations while being robust in the presence of 3D image noise, clutter, and surface occlusion. Of particular interest is the viability of using spin images to differentiate between two object classes in 3D imagery where there is significant intra-class diversity, e.g. to differentiate between wheeled and tracked vehicles. The specificity of spin map representations in differentiation of wheeled and tracked vehicles is statistically characterized. Using synthetic imagery of various wheeled and tracked vehicles, the class separability of wheeled vs. tracked vehicle spin image sets is nonparametrically quantified via entropic characterization as well as the Friedman-Rafsky two-sample test statistic. Additionally, class separability is analyzed in lower dimensional feature spaces generated via the Hotelling transform as well as a random projection method, comparing and contrasting the spin map class differentiation in the original and transformed data sets.
To support Autonomous Target Acquisition (ATA) evaluation and trades analysis, the Air Force Research Laboratory, Advanced Guidance Division (AFRL/MNG) located at Eglin AFB has incorporated a general-purpose performance evaluation system into its Modular Algorithm Concept Evaluation Tool (MACET). The MACET performance evaluation system may be used for active, passive, or multi-sensor ATA analysis. It consists of two main elements: a relational, multi-user database engine and a database client application, the Performance Evaluation Tool (PET). The database engine serves a set of databases that are used to capture, catalog, and archive test results for various algorithms under varying condition and environments. The MACET PET client application is a data mining tool for exploring the ATA test results in the databases, computing standard ATA detection and classification performance metrics (e.g., detection probability, detection reliability, false alarm rate, probability of correct classification, confusion matrices) on user defined subsets of data, calculating test case parameter statistics, and generating performance comparison plots.
The Air Force Research Lab, Advanced Guidance Division, AFRL/MNG located at Eglin AFB has expanded the capabilities of its Modular Algorithm Concept Evaluation Tool (MACET) for autonomous target acquisition (ATA) analysis to include an imagery truth editor for simultaneously displaying and working with multiple images of differing dimensionality and resolution. To support multi-sensor truthing, the MACET Truth Editor performs computer-assisted geo-spatial registration between multiple 2D images, or between 2D images and 3D images. The input images of overlapping scenes may be obtained from various sensor types (visible, passive infrared, laser radar (ladar), etc.) and taken at different sensor locations and orientations. Registration of 3D to 2D and 2D to 2D imagery pixels is made to a reference 3D coordinate system using `hints' provided by an analyst. Hints may include some combinations of the following to reach an approximate solution to the registration problem: marking of common points in each image, marking of horizon lines in 2D images, entry of imagery sensor characteristics (FOV, FPA layout, etc.), and entry of relative sensor location and orientation. The MACET Truth Editor has a consistent user interface that allows registration hints to be entered and truthing operations to be performed graphically.
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