Motion sequence analysis in the presence of figural cues

Published in final edited form as: Neurocomputing. 2015 January 5, 147: 485–491The perception of 3-D structure in dynamic sequences is believed to be subserved primarily through the use of motion cues. However, real-world sequences contain many figural shape cues besides the dynamic ones. We hypothesize that if figural cues are perceptually significant during sequence analysis, then inconsistencies in these cues over time would lead to percepts of non-rigidity in sequences showing physically rigid objects in motion. We develop an experimental paradigm to test this hypothesis and present results with two patients with impairments in motion perception due to focal neurological damage, as well as two control subjects. Consistent with our hypothesis, the data suggest that figural cues strongly influence the perception of structure in motion sequences, even to the extent of inducing non-rigid percepts in sequences where motion information alone would yield rigid structures. Beyond helping to probe the issue of shape perception, our experimental paradigm might also serve as a possible perceptual assessment tool in a clinical setting.The authors wish to thank all observers who participated in the experiments reported here. This research and the preparation of this manuscript was supported by the National Institutes of Health RO1 NS064100 grant to LMV. (RO1 NS064100 - National Institutes of Health)Accepted manuscrip

Correspondence Estimation from Non-Rigid Motion Information

The DIET (Digital Image Elasto Tomography) system is a novel approach to screen for breast cancer using only optical imaging information of the surface of a vibrating breast. 3D tracking of skin surface motion without the requirement of external markers is desirable. A novel approach to establish point correspondences using pure skin images is presented here. Instead of the intensity, motion is used as the primary feature, which can be extracted using optical flow algorithms. Taking sequences of multiple frames into account, this motion information alone is accurate and unambiguous enough to allow for a 3D reconstruction of the breast surface. Two approaches, direct and probabilistic, for this correspondence estimation are presented here, suitable for different levels of calibration information accuracy. Reconstructions show that the results obtained using these methods are comparable in accuracy to marker-based methods while considerably increasing resolution. The presented method has high potential in optical tissue deformation and motion sensing

A parallelogram four-frame model in 3-D motion and structure recovery using unified optical flow field approach

In this paper a new concept of the unified optical flow field (UOFF) for 3 D motion analysis from a stereo image sequence is implemented which is an extension of the temporal optical flow formulations developed by Horn and Schunck. A four frame model is established and a spatial optical flow is also introduced. In order to alleviate the problem of the strict requirement of two identical sensors in common stereo imagery a new imaging system is presented in this paper that needs only one camera, two plane mirrors and two switches to generate a phase shifted binocular sequence of images. It is shown that the structure and motion of the object surface can be reconstructed from the parallelogram four frame sequence set if the information of the experiment, including the focal length, the distance between two mirrors and the intersecting angle between two observing directions are known. Detailed computer simulations are presented and analyzed to illustrate the algorithm discussed

Workshop on multisensor integration in manufacturing automation

Journal ArticleMany people helped make the Workshop a success, but special thanks must be given to Howard Moraff for his support, and to Vicky Jackson for her efforts in making things run smoothly. Finally, thanks to Jake Aggarwal for helping to start the ball rolling

Models for Motion Perception

As observers move through the environment or shift their direction of gaze, the world moves past them. In addition, there may be objects that are moving differently from the static background, either rigid-body motions or nonrigid (e.g., turbulent) ones. This dissertation discusses several models for motion perception. The models rely on first measuring motion energy, a multi-resolution representation of motion information extracted from image sequences. The image flow model combines the outputs of a set of spatiotemporal motion-energy filters to estimate image velocity, consonant with current views regarding the neurophysiology and psychophysics of motion perception. A parallel implementation computes a distributed representation of image velocity that encodes both a velocity estimate and the uncertainty in that estimate. In addition, a numerical measure of image-flow uncertainty is derived. The egomotion model poses the detection of moving objects and the recovery of depth from motion as sensor fusion problems that necessitate combining information from different sensors in the presence of noise and uncertainty. Image sequences are segmented by finding image regions corresponding to entire objects that are moving differently from the stationary background. The turbulent flow model utilizes a fractal-based model of turbulence, and estimates the fractal scaling parameter of fractal image sequences from the outputs of motion-energy filters. Some preliminary results demonstrate the model\u27s potential for discriminating image regions based on fractal scaling

Stereo vision and motion analysis in complement.

by Ho Pui-Kuen, Patrick.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 57-59).Abstract also in Chinese.Acknowledgments --- p.iiList Of Figures --- p.vList Of Tables --- p.viAbstract --- p.viiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Moviation of Problem --- p.1Chapter 1.2 --- Our Approach and Summary of Contributions --- p.3Chapter 1.3 --- Organization of this Thesis --- p.4Chapter 2 --- Previous Work --- p.5Chapter 3 --- Structure Recovery from Stereo-Motion Images --- p.7Chapter 3.1 --- Motion Model --- p.8Chapter 3.2 --- Stereo-Motion Model --- p.10Chapter 3.3 --- Inferring Stereo Correspondences --- p.13Chapter 3.4 --- Determining 3D Structure from One Stereo Pair --- p.17Chapter 3.5 --- Computational Complexity of Inference Process --- p.18Chapter 4 --- Experimental Results --- p.19Chapter 4.1 --- Synthetic Images and Statistical Results --- p.19Chapter 4.2 --- Real Image Sequences --- p.21Chapter 4.2.1 --- House Model' Image Sequences --- p.22Chapter 4.2.2 --- Oscilloscope and Soda Can' Image Sequences --- p.23Chapter 4.2.3 --- Bowl' Image Sequences --- p.24Chapter 4.2.4 --- Building' Image Sequences --- p.27Chapter 4.3 --- Computational Time of Experiments --- p.28Chapter 5 --- Determining Motion and Structure from All Stereo Pairs --- p.30Chapter 5.1 --- Determining Motion and Structure --- p.31Chapter 5.2 --- Identifying Incorrect Motion Correspondences --- p.33Chapter 6 --- More Experiments --- p.34Chapter 6.1 --- Synthetic Cube' Images --- p.34Chapter 6.2 --- Snack Bag´ة Image Sequences --- p.35Chapter 6.3 --- Comparison with Structure Recovered from One Stereo Pair --- p.37Chapter 7 --- Conclusion --- p.41Chapter A --- Basic Concepts in Computer Vision --- p.43Chapter A.1 --- Camera Projection Model --- p.43Chapter A.2 --- Epipolar Constraint in Stereo Vision --- p.47Chapter B --- Inferring Stereo Correspondences with Matrices of Rank < 4 --- p.49Chapter C --- Generating Image Reprojection --- p.51Chapter D --- Singular Value Decomposition --- p.53Chapter E --- Quaternion --- p.5

Motion estimation using optical flow field

Over the last decade, many low-level vision algorithms have been devised for extracting depth from intensity images. Most of them are based on motion of the rigid observer. Translation and rotation are constants with respect to space coordinates. When multi-objects move and/or the objects change shape, the algorithms cannot be used. In this dissertation, we develop a new robust framework for the determination of dense 3-D position and motion fields from a stereo image sequence. The framework is based on unified optical flow field (UOFF). In the UOFF approach, a four frame mode is used to compute six dense 3-D position and velocity fields. Their accuracy depends on the accuracy of optical flow field computation. The approach can estimate rigid and/or nonrigid motion as well as observer and/or object(s) motion. Here, a novel approach to optical flow field computation is developed. The approach is named as correlation-feedback approach. It has three different features from any other existing approaches. They are feedback, rubber window, and special refinement. With those three features, error is reduced, boundary is conserved, subpixel estimation accuracy is increased, and the system is robust. Convergence of the algorithm is proved in general. Since the UOFF is based on each pixel, it is sensitive to noise or uncertainty at each pixel. In order to improve its performance, we applied two Kalman filters. Our analysis indicates that different image areas need different convergence rates, for instance. the areas along boundaries have faster convergence rate than an interior area. The first Kalman filter is developed to conserve moving boundary in optical How determination by applying needed nonhomogeneous iterations. The second Kalman filter is devised to compute 3-D motion and structure based on a stereo image sequence. Since multi-object motion is allowed, newly visible areas may be exposed in images. How to detect and handle the newly visible areas is addressed. The system and measurement noise covariance matrices, Q and R, in the two Kalman filters are analyzed in detail. Numerous experiments demonstrate the efficiency of our approach

Adaptive model-based motion estimation

A general discrete-time, adaptive, multidimensional framework is introduced for estimating the motion of one or several object features from their successive non-linear projections on an image plane. The motion model consists of a set of linear difference equations with parameters estimated recursively from a non-linear observation equation. The model dimensionality corresponds to that of the original, non-projected motion space thus allowing to compensate for variable projection characteristics such as panning and zooming of the camera. Extended recursive least-squares and linear-quadratic tracking algorithms are used to adaptively adjust the model parameters and minimize the errors of either smoothing, filtering or predicting the object trajectories in the projection plane. Both algorithms are derived using a second order approximation of the projection nonlinearities. All the results presented here use a generalized vectorial notation suitable for motion estimation of any finite number of object features and various approximations of the nonlinear projection. An example of motion estimation in a sequence of video frames is given to illustrate the main concepts