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A study was conducted to characterize discontinuous motion estimation as a multicomponent harmonic retrieval concern. Various frequency estimation methods were correlated with an explicit model for discontinuous motion. Results indicated that the framework supports the processes of discontinuous motion in a non-iterative manner. Findings also showed that it can calculate for velocities regardless of motion discontinuities and can be utilized in multimedia and video communication applications.

The ability to determine the viewing parameters of objects from their projections has enabled well established tomographic techniques to be employed in the three-dimensional reconstruction of objects from images obtained via modalities where the orientation of the objects cannot be controlled. A method is described for the determination of the viewing parameters of randomly acquired projections of asymmetric objects. It extends upon the common lines algorithm by determining the relative orientation of projections from the location of lines of intersection among the Fourier transforms of the projections in three-dimensional. Fourier space. A new technique for finding the lines of intersection in the presence of translational displacement, and for subsequently finding the translational displacement, is presented. The complete algorithm is described and its efficacy is demonstrated using real data. A new technique for dealing with noise is also discussed. Index Terms - Tomography, pose estimation, motion analysis, 3D reconstruction.

Although many algorithms have been developed for and motion estimation from range images, none are suited for use with scanning laser sensors. In this paper, the feature-based motion transformation model is restated to incorporate the nonzero pixel sampling rate of laser range cameras and a novel iterative, linear, feature-based technique for determining the three-dimensional (3-D) motion transformation of moving objects is developed using this new model. A technique is then presented which employs the motion recovered using the iterative algorithm to remove the structural distortion of the object in the range map. The performance of the motion recovery method is verified using simulated and experimental data. Index Terms - Motion recovery from range data, 3-D object reconstruction from range data.

An important issue in developing a model-based vision system is the specification of features that are invariant to viewing and scene conditions and also specific, i.e., the feature must have different values for different classes of objects. We formulate a new approach for establishing invariant features. Our approach is unique in the field since it considers not just surface reflection and surface geometry in the specification of invariant features, but it also takes into account internal object composition and state which affect images sensed in the nonvisible spectrum. A new type of invariance called Thermophysical Invariance is defined. Features are defined such that they are functions of only the thermophysical properties of the imaged objects. The approach is based on a physics-based model that is derived from the principle of the conservation of energy applied at the surface of the imaged object. Index Terms - Invariance, thermal image, physics-based computer vision, image understanding, model-based vision.

Thermophysical algebraic invariance formulation is a robust method for interpretation of invariant features of radiometrically uncalibrated infrared (IR) imagery for ATR and site change detection. The approach focuses on internal object composition and thermophysical properties of imaged objects. Invariance to ambient scene conditions, ability to classify objects and focus on phenomena affecting long-wave IR imagery are the special features of the method. Symmetric alphastable models can adequately model all these features. Results of real IR imagery and ground truth data are presented.

This paper addresses the problem of mobile robot self-localization given a polygonal map and a set of observed edge segments. The standard approach uses interpretation tree search with pruning heuristics to match observed edges to map edges. Our approach introduces a preprocessing step in which the map is decomposed into view-invariam regions (VIR's). The VIR decomposition captures information about map edge visibility, and can be used for a variety of robot navigation tasks.

Conventional image motion estimation schemes use spatiotemporal algorithms which assume that images move at a constant velocity. Such estimation schemes prove to be inappropriate when images move at a varying velocity. However, a novel image motion estimation approach has been developed with uses a polynomial or a trigonometric polynomial algorithm. Tests on the approach reveal that it produces more accurate estimates.

Motion smear is an important visual cue for motion perception by the human vision system (HVS). However, in image analysis research, exploiting motion smear has been largely ignored. Rather, motion smear is usually considered as a degradation of images that needs to be removed. In this paper, we establish a computational model that estimates image motion from motion smear information - "motion from smear." In many real situations, the shutter of the sensing camera must be kept open long enough to produce images of adequate signal-to-noise ratio (SNR), resulting in significant motion smear in images. We present a new motion blur model and an algorithm that enables unique estimation of image motion. A prototype sensor system that exploits the new motion blur model has been built to acquire data for "motion-from-smear." Experimental results on images with both simulated smear and real smear, using our "motion-from-smear" algorithm as well as a conventional motion estimation technique, are provided. We also show that temporal aliasing does not affect "motion-from-smear" to the same degree as it does algorithms that use displacement as a cue. "Motion-from-smear" provides an additional tool for motion estimation and effectively complements the existing techniques when apparent motion smear is present. Index Terms - Motion estimation, motion smear (blur), motion-from-smear.

This paper analyzes the performance of cepstral approaches for solving the stereo correspondence problem. A quantitative analysis of the effects of noise, foreshortening differences, and photometric variations on existing cepstral correspondence techniques is presented. A modified approach that is less sensitive to these effects is developed for textured scenes, and analytical arguments for its robustness are developed. The results of a comparative study of the new cepstral technique, the original cepstral algorithm and the cross-correlation approach are shown and discussed. The performance of the new method is experimentally verified on textured surfaces. Index Terms - Cepstrum, image correspondence, stereopsis, depth estimation, foreshortening correction.

Object recognition requires robust and stable features that are unique in feature space. Lie group analysis provides a constructive procedure to determine such features, called invariants, when they exist. Absolute invariants are rare in general, so quasi-invariants relax the restrictions required for absolute invariants and, potentially, can be just as useful in real-world applications. This paper develops the concept of a dominant-subspace invariant, a particular type of quasi-invariant, using the theory of Lie groups. A constructive algorithm is provided that fundamentally seeks to determine an integral submanifold which, in practice, is a good approximation to the orbit of the Lie group action. This idea is applied to the long-wave infrared problem and experimental results are obtained supporting the approach. Other application areas are cited. Index Terms--Dominant-subspace invariants, Lie group analysis, principal basis, quasi-invariants, thermophysical invariance, thermophysical model.

We address the problem of detection of targets obscured by a forest canopy using an ultrawideband (UWB) radar. The forest clutter observed in the radar imagery is a highly impulsive random process that is more accurately modeled with the recently proposed class of alpha-stable processes as compared with Gaussian, Weibull, and K-distribution models. With this more accurate model, segmentation is performed on the imagery into forest and clear regions. Further, a region-adaptive symmetric alpha stable (S[Alpha]S) constant false-alarm rate (CFAR) detector is introduced and its performance is compared with the Weibull and Gaussian CFAR detectors. The results on real data show that the S[Alpha]S CFAR performs better than the Weibull and Gaussian CFAR detectors in detecting obscured targets.