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.
The 'curse of dimensionality' has limited the application of statistical modeling techniques to low-dimensional spaces, but typical data usually resides in high-dimensional spaces (at least initially, for instance images represented as arrays of pixel values). Indeed, approaches such as Principal Component Analysis and Independent Component Analysis attempt to extract a set of meaningful linear projections while minimizing interpoint distance distortions. The counterintuitive yet effective random projections approach of Johnson and Lindenstrauss defines a sample-based dimensionality reduction technique with probabilistically provable distortion bounds. We investigate and report on the relative efficacy of two random projection techniques for Synthetic Aperture Radar images in a classification setting.
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.
This investigation discusses the challenge of target classification in terms of intrinsic dimensionality estimation and selection of appropriate feature manifolds with object-specific classifier optimization. The feature selection process will be developed via nonlinear characterization and extraction of the target-conditional manifolds derived from the training data. We investigate defining the feature space used for classification as a class-conditioned nonlinear embedding, i.e., each training and test image is embedded in a target-specific embedding and the resultant embeddings are used for statistical characterization. We compare and contrast this novel embedding technique with Principal Component Analysis. The α-Jensen Entropy Difference measure is used to quantify the object-conditioned separation between the target distributions in the feature spaces. We discuss and demonstrate the effect of feature space extraction on classification efficacy.
An in-line, non-destructive process is being developed for characterizing polycrystalline thin-film and other large area electronic devices using computer vision based imaging of the manufacturing and inspection steps during the device fabrication process. This process is being applied specifically to Cadmium Telluride/Cadmium Sulfide (CdTe/CdS) thin film, polycrystalline solar cells. Our process involves the acquisition of reflective, transmission and electroluminescence (EL) intensity images for each device. The EL intensity images have been processed by use of a modified median cut segmentation. The processed images reveal different gray level regions corresponding to different intensities of EL originating from radiative recombination events occurring within a biased solar cell. Higher efficiency devices show a more uniform intensity distribution in contrast with lower efficiency devices. The uniform intensity regions are made up of gray level intensity values found near the mean of the histogram distribution these are identified as regions of good device performance and are attributed to better material quality and processing. Low intensity regions indicate either material defects or errors in processing. This novel characterization process and analysis are providing new insights into the causes of poor performance in CdTe-based solar cells.
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