Http://journalofvision.org/10/3/17/

Dynamic random dot targets consisting of many localized motion vectors have been used to study the pooling of local motion signals into a global motion percept (Williams and Sekuler, 1984). In such displays, the dots are displaced with a constant step size and the direction of motion for each dot is chosen at random from a specified distribution. When the distribution extends over 360 deg, the display consists only of local random motion of individual dots and no coherent motion is reported. However, when the distribution is less than 360 deg (biased), the stimulus appears to flow in a single direction. We examined the effects of reducing the number of directionally selective (DS) cortical neurons on this integration process. Normal cats and cats with severely reduced proportions of DS neurons were trained on 2 direction discrimination tasks. The discrimination of opposite directions was examined while varying either the range of directions of local motion, or the proportion of dots moving with biased distribution. When all dots in the display were directionally biased, cats with reduced numbers of DS neurons performed the task as well as normal cats and humans (threshold range: 280-320 deg). However, when the proportion of biased dots decreased, these animals had severe deficits. Thus, in the absence of noise, even a very small number of DS neurons can perform spatial pooling of local directional signals, and support normal discrimination of opposite directions. However, a full complement of directional detectors appears necessary when the motion signal is masked by noise. The discrimination of small differences in direction revealed far more severe deficits, even when all the dots in the display were directionally biased (no noise).(ABSTRACT TRUNCATED AT 250 WORDS)

In Versatile Video Coding (VVC), local affine motion compensation (LAMC) is adopted to handle complex motions, such as rotation and zooming. However, it is inefficient to use LAMC to handle the global motion due to the following two reasons. First, the use of LAMC may lead to some extra bit cost on the affine motion model parameters. Second, the precision of LAMC is restricted by the MV precision of the control points. Therefore, in this paper, we propose a global homography motion compensation (GHMC) framework to better characterize the global motion. For each coding block, an extra mode is added to perform motion compensation based on an 8-parameter global homography motion model. In addition, an extrapolation scheme is designed to derive the parameters from reference frames to save the bit cost for signaling them. The proposed framework is implemented into the VVC reference software VTM-6.0. Experimental results show that, on average, 0.69% and 0.66% BD-rate reduction is achieved under Low Delay P and Low Delay B configurations, respectively, for sequences with rich complex global motions.

In this paper, we present an improved approach to predictive video decoding based on global and local motion reliability. The framework consists of three processing stages. Global motion (GM) estimation and motion reliability analysis are the key components in the first stage, where we model global motion and refine the MV field. In the second stage, we predict local and global motion for the target frame, and determine corresponding weights based on the Linear Minimum Mean Square Error (LMMSE) criterion. Finally in the third stage, a temporal interpolator is applied to compose two future frames, which are linearly combined to form the final predicted frame. Our results indicate the proposed method achieves better visual quality compare to other state-of-the-art predictive decoding approaches, particularly in sequences involving moving camera and objects.

This paper presents two novel algorithms for estimating the (local and global) motion in a series of range images based on a polynomial expansion. The use of polynomial expansion has been quite successful in estimating optical flow in 2D imagery, but has not been used extensively in 3D or range imagery. In both methods, each range image is approximated by applying a high-order polynomial expansion to local neighborhoods within the range image. In the local motion algorithm, these approximations are then used to derive the translation or displacement estimation within the local neighborhoods from frame to frame within the series of range images (also known as range image flow). An iterative method for computing the local translations is presented. In the global motion algorithm, a global motion model framework is utilized to compute a global motion estimation based on the polynomial expansion of the range images. We evaluate the algorithms on several real-world range image sequences with promising results.

We present a novel technique for the estimation of global nonrigid motion of an object's boundary without using point correspondences. A complete description of the motion of an object's boundary involves specifying a displacement vector at each point of the boundary. Such a description provides a large amount of information, which needs to be processed further for study of the global characteristics of the deformation. Nonrigid motion can be studied hierarchically in terms of global nonrigid motion and point-by-point local nonrigid motion. The technique presented gives a method for estimating a global affine or polynomial transformation between two object boundaries. The novelty of the technique lies in the fact that it does not use any point correspondences. Our method uses hyperquadric models to model the data and estimate the global deformation. We show that affine or polynomial transformation between two datasets can be recovered from the hyperquadric parameters. The usefulness of the technique is twofold. First, it paves the way for viewing nonrigid motion hierarchically in terms of global and local motion. Second, it can be used as a front end to other motion analysis techniques that assume small motion. For instance, most nonrigid motion analysis algorithms make some assumptions on the type of nonrigid motion (conformal motion, small motion, etc.) that are not always satisfied in practice. When the motion between two datasets is large, our algorithm can be used to estimate the affine transformation (which includes scale and shear) or a polynomial transformation between the two datasets, which can then be used to warp the first dataset closer to the second so as to satisfy the small-motion assumption. We present experimental results with real and synthetic two- and three-dimensional data.

The processing of motion changes throughout the visual hierarchy, from spatially restricted ‘local motion’ in early visual cortex to more complex large-field ‘global motion’ at later stages. Here we used functional magnetic resonance imaging (fMRI) to examine spatially selective responses in these areas related to the processing of random-dot stimuli defined by differences in motion. We used population receptive field (pRF) analyses to map retinotopic cortex using bar stimuli comprising coherently moving dots. In the first experiment, we used three separate background conditions: no background dots (dot-defined bar-only), dots moving coherently in the opposite direction to the bar (kinetic boundary) and dots moving incoherently in random directions (global motion). Clear retinotopic maps were obtained for the bar-only and kinetic-boundary conditions across visual areas V1–V3 and in higher dorsal areas. For the global-motion condition, retinotopic maps were much weaker in early areas and became clear only in higher areas, consistent with the emergence of global-motion processing throughout the visual hierarchy. However, in a second experiment we demonstrate that this pattern is not specific to motion-defined stimuli, with very similar results for a transparent-motion stimulus and a bar defined by a static low-level property (dot size) that should have driven responses particularly in V1. We further exclude explanations based on stimulus visibility by demonstrating that the observed differences in pRF properties do not follow the ability of observers to localise or attend to these bar elements. Rather, our findings indicate that dorsal extrastriate retinotopic maps may primarily be determined by the visibility of the neural responses to the bar relative to the background response (i.e. neural signal-to-noise ratios) and suggests that claims about stimulus selectivity from pRF experiments must be interpreted with caution.

The amblyopic deficit for global motion is spatial scale invariant

To remove the vibration in video content, a video stabilization approach is proposed in this paper. Video stabilization is completed by local rotational motion model in our approach. The local rotational motion is derived from rotation-based block matching method. The global rotational motion is identified by a local rotational motion model. Particularly, global rotational motion is derived by computing the variance of local rotational motion and its direction. Finally, the global rotational motion is smoothed by calculating the motion of neighboring frames. The experimental results demonstrate that the proposed video stabilization approach can produce well smooth videos with smooth rotational motion in various videos. The comparison also shows our superior performance on video stabilization.

Joint ground moving-target tracking identification is a crucial task in a modern combat operation. Due to the entirely different environment, ground moving-target tracking is quite different from airborne target tracking. A major difference lies in target modelling. In airborne target tracking, a target is usually treated as a point, while for ground target tracking, a target is considered a rigid body. Two approaches for 2D rigid-body target modelling are proposed. Equipped with ground moving-target indicator and high-resolution range sensors, the new approaches effectively explore the concepts of local and global motions of a rigid body. The kinematics of a global motion is described by a constant acceleration model, and a local motion is modelled by the pivoting centre and pseudocentre approaches. The proposed models are implemented by the extended Kalman filter with and without a probabilistic data association filter. The simulation results show that the proposed approaches not only correctly track a rigid-body target in a complicated scenario but also simultaneously report its structural information.

A new algorithm to distinguish the foreground from compressed videos is proposed in this paper. Local motion, which is estimated from the residual between the original motion and the global motion, is one of the strongest influences on visual attention. Global motion is modeled by four parameters related to camera pan, tilt, zoom and rotation. The initial parameters are obtained from the least-squares method and updated iteratively using the Levenberg-Marquardt algorithm. Temporal and spatial filters are also introduced to revise the final global motion. In addition, DC coefficients are employed to refine the result based on the local motion. Experiments show that the proposed algorithm can segment foreground effectively with a largely reduced computational complexity, as DC coefficients and motion vectors can easily be extracted from compressed videos.

Following a person is a fundamental requirement for human-robot interaction. In this paper we propose a novel tracking approach for robust human feet tracking which integrates human locomotion into tracking algorithms. The vertical displacement between the two feet is analyzed and we observe that this displacement during the walking cycle is close to a modulated cosine waveform. Based on this, we propose an adaptive model for the human walking pattern. We divide the motion of the human feet into local motion and global motion. The local motion is modeled by a modified cosine wave that updates along time. Global motion is estimated by the continuity between successive frames. This model is combined with particle filtering to guide the searching of the feet. A 2D Gaussian mask is generated according to the predicted position estimated by the motion model and used to modify the weight of the particles. Experiments are implemented in several human walking videos and the algorithm is evaluated against the generic particle filtering method. Results show that the feet can be tracked successfully with significant improvements compared to the generic particle filtering method.

Cardiac cine magnetic resonance imaging (CMR) is the clinical reference standard for assessing ventricular function, myocardial motion, and valvular abnormalities. However, its diagnostic reliability is often compromised by global motion artifacts, including translation, rotation, and contraction/expansion, which degrade frame-to-frame alignment and impair quantitative accuracy. Traditional correction strategies, such as ECG gating, breath-hold acquisitions, and retrospective registration, remain limited by patient compliance and sensitivity to irregular rhythms, motivating the need for advanced data-driven approaches.In this work, we present a deep learning framework for global motion correction in cine CMR that integrates dual displacement field estimation, a global motion transformation layer, and a hierarchical feature encoding–decoding network with frequency-domain channel attention. The model predicts bidirectional motion fields between moving and fixed frames, enforces temporal consistency, and explicitly corrects for cardiac-specific global displacements. Evaluation was performed on the CMRxRecon2024 dataset, comprising 180 cine CMR subjects with 12 motion states per scan. Experimental results demonstrate that the proposed method achieves a normalized root mean squared error (NRMSE) of 0.098, outperforming the state-of-the-art Coarse-to-Fine Diffusion baseline (NRMSE = 0.1225). These findings confirm that explicitly modeling global cardiac motion within a diffusion-informed encoder–decoder architecture substantially improves reconstruction quality and temporal alignment. The proposed framework advances cine CMR motion correction and has the potential to enhance downstream clinical assessment of ventricular function and valvular pathology.

1.19 - Direction-Selective Cells

To respond to movements of others and understand the intention of others' actions, it is important to accurately extract motion information from body movements. Here, using original and spatially scrambled point-light biological motions in upright and inverted orientations, we investigated the effect of global and local biological motion information on speed perception and sensitivity. The speed discrimination task revealed that speed sensitivity was higher for the original than for scrambled stimuli (Experiment 1) and higher for upright than for inverted stimuli (Experiment 2). Perceived motion speed was slower for the original than for scrambled stimuli (Experiment 2), but regardless of the orientation of the display (Experiment 1). A subsequent experiment comparing different scrambled stimuli of the same actions showed that the higher speed discrimination sensitivity to upright stimuli was preserved even in the scrambled biological motions (Experiment 3). Taken together, our findings suggest that perception of the speed of biological movements emanates from both global and local biological motion signals.

Human perception of visual motion is thought to involve two stages--estimation of local motion (i.e., of small features) and global motion (i.e., of larger objects)--identified with cortical areas V1 and MT, respectively. We asked if poor discrimination of oblique compared to cardinal directions (the oblique effect for motion; OEM) reflects a deficit in local or in global motion processing. We used an equivalent noise (EN) paradigm--where one measures direction discrimination thresholds in the presence of directional variability--to quantify local and global limits. We report that the OEM diminishes with increasing directional variability, indicating that global motion processing (the number of local motion signals pooled) is equal across all directions and that the OEM is attributable to anisotropies in local motion processing. To investigate the origin of this effect, we measured local motion statistics from natural movies (filmed from the point of view of a walking observer). This analysis reveals that the distribution of local directional energy on the oblique directions tends to be broader, and frequently more asymmetric, than on the cardinal directions. If motion detectors are optimized to deal with our visual world then such anisotropies likely explain the local nature of the OEM.