Motion discontinuities arise when there are occlusions or multiple moving objects in the scene that is imaged. Conventional regularization techniques use smoothness constraints but are not applicable to motion discontinuities. In this paper, we show that discontinuous (or multiple) motion estimation can be viewed as a multicomponent harmonic retrieval problem. From this viewpoint, a number of established techniques for harmonic retrieval ran be applied to solve the challenging problem of discontinuous (or multiple) motion. Compared with existing techniques, the resulting algorithm is not iterative, which not only implies computational efficiency but also obviates concerns regarding convergence or local minima. It also adds flexibility to spatio-temporal techniques which have suffered from lack of explicit modeling of discontinuous motion. Experimental verification of our framework on both synthetic data as well as real image data is provided.

The ability to determine the viewing parameters of objects from their projections has enabled well established tomographic techniques to be employed in the 3D 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 3D 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.

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. A new technique for dealing with noise is also presented. The complete algorithm is described and its efficacy is demonstrated using real data. This technique may be applied to the three-dimensional reconstruction of viruses, molecules, and cells from in vivo images. It also has many other applications including the reconstruction of underwater scenes, radioastronomy, geoseismic analysis, and portable radiography for medical diagnosis and industrial inspection.

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, the authors 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. The authors 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 the authors' "motion-from-smear" algorithm as well as a conventional motion estimation technique, are provided. The authors 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.

Image motion estimation using the spatiotemporal approach has largely relied on the constant velocity assumption, and thus becomes inappropriate when the velocity of the imaged scene or the camera changes during the data acquisition time. Using a polynomial or a trigonometric polynomial model for the time variation of the image motion, spatiotemporal algorithms are developed in this paper to handle time-varying (but space-invariant) motion. Under these models, it is shown that time-varying image motion estimation is equivalent to parameter estimation of one-dimensional (1-D) polynomial phase or phase-modulated signals, which allows one to exploit well-established results in radar signal processing. When compared with alternative approaches, the resulting motion estimation algorithms produce more accurate estimates. Simulation results are provided to demonstrate the proposed schemes.

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-invariant regions (VIRs). The VIR decomposition captures information about map edge visibility, and can be used for a variety of robot navigation tasks.

Motion discontinuities arise when there are occlusions or multiple moving objects in the scene that is imaged. We show that discontinuous (or multiple) motion estimation can be viewed as a multicomponent harmonic retrieval problem. Compared with existing techniques, the resulting algorithm is not iterative, which not only implies computational efficiency but also obviates concerns regarding convergence or local minima. It also adds flexibility to spatio-temporal techniques which have suffered from lack of explicit modeling of discontinuous motion.

Image motion estimation usually relies on using interframe displacement as the information cue for motion. Another cue has been proposed, namely motion smear. An algorithm used to compute the image motion from the smear information ("motion-from-smear") was proposed by Chen, Nandhakumar and Martin (see Proc. IEEE Intern. Conf. Image Proc., vol.III, p.761-765, Austin, Texas, November 1994) where the algorithm was verified with simulated motion smeared images. This paper extends the work of Chen et al. by presenting (1) experimental results on real data, (2) a feasible sensor design and (3) a generalization to space-varying motion.

Image motion estimation using the spatio-temporal approach has largely relied on the constant velocity assumption. Spatio-temporal algorithms are developed in this paper to handle time-varying motion. Time-variation is modeled either as a polynomial or as a periodic function of time. Under these models, it is shown that time-varying image motion estimation is equivalent to parameter estimation of one-dimensional polynomial phase or phase modulated signals. Simulation results are provided to illustrate the proposed algorithm.>

Motion smear is an important visual cue for the human vision system (HVS) to perceive motion. However, in image analysis research, using motion smear as a valuable visual cue for motion estimation has remained largely ignored. Rather, motion smear is usually considered as a degradation on images that need to be removed. A computational model has been established to estimate image motion from motion smear information. Both theoretical derivation and experimental results are provided.>

A method for determining the relative view orientation of randomly acquired projections of asymmetric objects is described. It extends the projection slice theorem by determining the relative orientation of projections by the location of lines of intersection among the Fourier transforms of the projections in 3D Fourier space. The complete algorithm is described, and its efficacy is demonstrated using real data.>

The ability to determine the relative view orientation 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. Most techniques used to determine particle orientation from projections assume that the particle exhibits some form of symmetry. A method is described for the determination of the relative view orientation of randomly acquired projections of asymmetric objects. It extends upon the projection slice theorem by determining the relative orientation of projections by the location of lines of intersection among the Fourier transforms of the projections in three-dimensional Fourier space. The complete algorithm is described and its efficacy is demonstrated using real data.>

Presents a statistically robust approach for multisensory computer vision. Specifically energy exchange model parameters used to inter-relate thermal and visual imagery are reliably computed. The approach extracts physically meaningful estimates of internal object properties which are useful for automated object recognition. The robust technique minimizes sensitivity to outliers caused by segmentation errors and misregistration which are endemic to multisensor fusion.>

This paper describes a real-time vision system that enhances teleoperation of a servicing tool used an nuclear power plant heat exchangers. The vision system i s used to track the posilion of the tool as it moves over a sheet of tube-ends. An efficient image feature extraction scheme is deceloped to locate the tube-end centers in the image. A map-based strategy is adopted for position estimation. This scheme minimizes cumulative errors in the estimates. The system incorporates a novel method for foreshortening correction which is applied prior to map referencing. The resulting IOW cost system has been tested on syn,thetic and real data. Performance results are indicated.