Showing papers on "Motion analysis published in 1985"

Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects

Abstract: A new approach for the interpretation of optical flow fields is presented. The flow field, which can be produced by a sensor moving through an environment with several independently moving, rigid objects, is allowed to be sparse, noisy, and partially incorrect. The approach is based on two main stages. In the first stage, the flow field is partitioned into connected segments of flow vectors, where each segment is consistent with a rigid motion of a roughly planar surface. In the second stage, segments are grouped under the hypothesis that they are induced by a single, rigidly moving object. Each hypothesis is tested by searching for three-dimensional (3-D) motion parameters which are compatible with all the segments in the corresponding group. Once the motion parameters are recovered, the relative environmental depth can be estimated as well. Experiments based on real and simulated data are presented.

Automatic analysis of the motion of microorganisms

Abstract: SUMMARY An automatic, interpretative, off-line computer-television-microscope system for studying the behaviour of microorganisms is presented. The equipment should be available even in relatively modestly equipped laboratories. The movement of microorganisms freely swimming in the chamber of a depression slide is recorded on a video tape with the aid of a microscope and an infrared TV camera. The images are acquired by means of a frame grabber in an image controller and elaborated by a PDP 11/23 computer. The procedures used for data processing are discussed in detail. Trajectories reconstructed by the computer are shown to be exactly superimposed to the real ones, and the motion parameters can be easily determined.

Finding Trajectories of Feature Points in a Monocular Image Sequence.

Abstract: Identifying the same physical point in more than one image, the correspondence problem, is vital in motion analysis. Most research for establishing correspondence uses only two frames of a sequence to solve this problem. By using a sequence of frames, it is possible to exploit the fact that due to inertia the motion of an object cannot change instantaneously. By using smoothness of motion, it is possible to solve the correspondence problem for arbitrary motion of several nonrigid objects in a scene. We formulate the correspondence problem as an optimization problem and propose an iterative algorithm to find trajectories of points in a monocular image sequence. A modified form of this algorithm is useful in case of occlusion also. We demonstrate the efficacy of this approach considering synthetic, laboratory, and real scenes.

Two view motion analysis under a small perturbation

Abstract: Given a set of corresponding points from a moving object is two perspective projection images. The paper completely solves the two view motion problem. We show how to use the corresponding point set to determine mode of motion, rotation, translation orientation and relative depths. Also we give a noise robust algorithm which works well under small perturbations.

Two view motion analysis, stereo vision and a moving camera's positioning their equivalence and a new solution procedure

Abstract: The three problems: Two view motion analysis, stereo vision and determining a moving camera's position are all the same problem. We explain their equivalence and introduce a noise robust procedure for solving these problems.

Method of using FLIR for motion analysis

Abstract: A method is provided for recording the motions of a subject while exercising or otherwise engaged in physical activity. A far-infrared detection system is used to record the movements of key positions on the subject to simplify subsequent analysis.

Analysis of a Time-Varying Scene Observed with a Digital CCD Camera

Abstract: The analysis of digital solar flare images obtained with a CCD camera, their representation in graphical form, and the various corrections (calibrations, image motion, atmospheric conditions) to be applied to the data are presented. Specially treated is the description of time-varying scenes and the possibility of reducing the information of the two-dimensional images to one value in order to study photometric parameters as a function of time. It is discussed in detail how original raw data can be restored by Fourier-filtering techniques in space and time yielding an almost noise-free light curve. All data analysis has been done on a large general-purpose computer and not on a specialized picture analysis system which usually is not commonly available. Though the object of our study is the sun, the analysis presented here is equally well applicable to any two-dimensional digital image.

Recognition of Moving Objects Using Feature Signatures

Abstract: An important application of machine vision systems is the recognition of known three-dimensional objects. A major difficulty arises when two or more objects project the same or similar two-dimensional image, often resulting in misclassification and degradation of system performance. The changes in images which result from the motion of objects provide a source of three-dimensional information which can greatly aid the classification process, but this three-dimensional analysis is computationally complex and subject to many sources of error. This work develops a methodology which utilizes the information derived from the apparent changes in object features over time to facilitate the recognition task, without the need to actually recover the three-dimensional structure of the objects under view. The basic approach is to generate a ``feature signature'' by combining the feature measurements of the individual regions in a long sequence of images. The static information in the individual frames is analyzed along with the temporal information from the entire sequence. These techniques are particularly applicable in situations where static image processing methods cannot discriminate between ambiguous objects. Two example implementations are presented to illustrate the application of the techniques of object recognition using motion information.

Further Results On Fundamental Limitations Of Conventional Target Motion Analysis

Abstract: This paper examines the interaction of velocity information with the bearings-only target motion analysis problem Theoretical results, obtained by a Cramer-Rao lower bound analysis are presented Experimental results, obtained using a maximum likelihood estimation algorithm and simulated data agree with theory