Thesis (Ph. D.) in Physics--University of Maine, 2003. / Includes vita. Includes bibliographical references (leaves 129-143).

Thesis (Ph. D.)--College of Computing, Georgia Institute of Technology, 2004. Directed by Aaron Bobick. / Vita. Includes bibliographical references (leaves 119-123).

Thesis (Ph.D.)--City University of Hong Kong, 2009. / "Submitted to Department of Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 145-161)

An important problem in electromagnetics is that of extracting, interpreting, and exploiting scattering mechanisms from the scattered field of a target. Termed “features”, these physics-based descriptions of scattering phenomenology have many and diverse applications such as target identification, classification, validation, and imaging. In this dissertation, the feature extraction, analysis, and exploitation of both synthetic and measured multidimensional radar signatures are investigated. Feature extraction is first performed on simulated data of the highfrequency electromagnetics solver Xpatch. The scattered, far-field of an electrically large target is well-approximated by a discrete set of points known as scattering centers. Xpatch yields three-dimensional (3D) scattering centers of a target one aspect angle at a time by using the shooting and bouncing ray technique and a computer-aided design (CAD) model of the target. The feature extraction technique groups scattering centers across multiple angles that pertain to the same scattering mechanism. Using a nearest neighbor clustering algorithm, this association is carried-out in a multidimensional grid of scattering center angle, bounce, and spatial location, wherein distinct scattering mechanisms are assumed to be non-overlapping. Synthetic monostatic and bistatic feature sets are extracted and analyzed using this algorithm. Additionally, feature sets are exploited to assist humans in electromagnetic CAD model validation. The generation of target CAD models is a challenging, resource-limited, and human-experience-based process. Target features extracted from a CAD model in question are compared individually to measured data from the physical target by projection of their radar signatures. CAD model disagreements such as missing, added, or dimensionally inaccurate components, as well as measurement imperfections are analyzed. Target traceback information of the features identifies flawed areas of the model. The projection value quantifies the degree of disagreement. The feature extraction methodology is next modified for measured radar signatures which lack readily available scattering center and bounce information. First, many ground plane synthetic aperture radar images of overlapping, limited apertures in azimuth are formed from the measurement data. Then, two-dimensional scattering centers of all images are estimated using a modified CLEAN algorithm. Feature extraction is lastly performed as with Xpatch data, though a reduction in grid dimensionality and orthogonality occurs. Finally, measured feature sets are exploited for sparse elevation 3D imaging and improved CAD model validation. The first application estimates the truth 3D scattering center of each feature using linear least squares to then visualize a composite 3D image of the target. The second application projects both synthetic and measured feature radar signatures to mitigate errors from the intersection of features in the combined measurement signature. / text

This dissertation describes a methodology for automated recognition of complex human activities. The dissertation presents a general framework which reliably recognizes various types of high-level human activities including human actions, human-human interactions, human-object interactions, and group activities. Our approach is a description-based approach, which enables a user to encode the structure of a high-level human activity as a formal representation. Recognition of human activities is done by semantically matching constructed representations with actual observations. The methodology uses a context-free grammar (CFG) based representation scheme as a formal syntax for representing composite activities. Our CFG-based representation enables us to define complex human activities based on simpler activities or movements. We have constructed a hierarchical framework which automatically matches activity representations with input observations. In the low-level of the system, image sequences are processed to extract poses and gestures. Based on the recognition of gestures, the high-level of the system hierarchically recognizes complex occurring human activities by searching for gestures that satisfies the temporal, spatial, and logical structure described in the representation. The concept of hallucinations and a probabilistic semantic-level recognition algorithm is introduced to cope with imperfect lower-layers. As a result, the system recognizes human activities including 'fighting', 'assault', 'a person leaving a suitcase', and 'a group of thieves stealing an object from owners', which are high-level activities that previous systems had difficulties. The experimental results show that our system reliably recognizes sequences of various types of complex human activities with a high recognition rate. / text

Not available / text

No description available.

No description available.

No description available.

No description available.