Pure Optic Flow Research Articles

Control and recalibration of path integration in place cells using optic flow.

Hippocampal place cells are influenced by both self-motion (idiothetic) signals and external sensory landmarks as an animal navigates its environment. To continuously update a position signal on an internal 'cognitive map', the hippocampal system integrates self-motion signals over time, a process that relies on a finely calibrated path integration gain that relates movement in physical space to movement on the cognitive map. It is unclear whether idiothetic cues alone, such as optic flow, exert sufficient influence on the cognitive map to enable recalibration of path integration, or if polarizing position information provided by landmarks is essential for this recalibration. Here, we demonstrate both recalibration of path integration gain and systematic control of place fields by pure optic flow information in freely moving rats. These findings demonstrate that the brain continuously rebalances the influence of conflicting idiothetic cues to fine-tune the neural dynamics of path integration, and that this recalibration process does not require a top-down, unambiguous position signal from landmarks.

Vection induced by low-level motion extracted from complex animation films.

This study examined the contributions of low-, mid- and high-level visual motion information to vection. We compared the vection experiences induced by hand-drawn and computer-generated animation clips to those induced by versions of these movies that contained only their pure optic flow. While the original movies were found to induce longer and stronger vection experiences than the pure optic flow, vection onsets were not significantly altered by removing the mid- and high-level information. We conclude that low-level visual motion information appears to be important for vection induction, whereas mid- and higher-level display information appears to be important for sustaining and strengthening this vection after its initial induction.

Abnormal Crowd Motion Detection with Hidden Markov Model

stations,etc. With the increasing demand of surveillance of various human activities, an efficient automated surveillance system to detect anomalies has become important. There is a survey on visual surveillance in [1], and a lot of problems have not resolved in surveillance applications nowadays as discussed in some papers [2]. Crowd feature extraction and crowd modeling are two important approaches of crowd analysis in Video surveillance. Most detection methods use motion features such as image gradient, texture information, optical flow, or spatial-temporal volume characteristics [3-4]. These features allow us to analyze dense crowds using characteristics of a crowd rather than individuals. Some works in the analysis of crowds usually assume that individuals can be tracked and identified in the crowd [5]. For example, the method proposed in [6] combines the inter-images difference based on entropy image and optical flow computed by a local method with a hierarchical coarse-to-fine optical flow estimation, and this method has a big computation load. Once motion features are extracted, another approach is to model them to represent normal motion behaviors. Abnormal crowd motion is defined as a sudden change or irregularity in a crowd motion. Suppose there exists a mathematical model describing the normal behaviors in the crowd videos, anomaly detection is done by modeling normal behaviors, and a crowd behavior is declared as anomaly if its characteristic does not comply with the learning model. Many approaches use parametric models, however, in parametric approach the data characteristics have to be approximated with a standard distribution and in many cases such approximations do not work well. In [7], Social Force model is used to detect and localize abnormal behaviors in crowd videos by Mehran et al. Their result shows the approach has a better performance than similar approach based on pure optical flow. Andrade et al. [8] combined spectral clustering, Principal Components Analysis (PCA) and Hidden Markov Model (HMM) to detect the crowd emergency scenarios, but the eigenvectors obtained by dimensionality reduction with PCA on the optical flow fields can not adequately reflect the motion, and this approach was only tested in simulated data. Our approach derives variations of motion patterns though direction distribution of the crowd motion obtained by optical flow and these variations are encoded with HMM Abnormal Crowd Motion Detection with Hidden Markov Model Dongping Zhang, Yafei Lu, Xinghao Jiang, Huailiang Peng

Control Grid Motion Estimation for Efficient Application of Optical Flow

Motion estimation is a long-standing cornerstone of image and video processing. Most notably, motion estimation serves as the foundation for many of today's ubiquitous video coding standards including H.264. Motion estimators also play key roles in countless other applications that serve the consumer, industrial, biomedical, and military sectors. Of the many available motion estimation techniques, optical flow is widely regarded as most flexible. The flexibility offered by optical flow is particularly useful for complex registration and interpolation problems, but comes at a considerable computational expense. As the volume and dimensionality of data that motion estimators are applied to continue to grow, that expense becomes more and more costly. Control grid motion estimators based on optical flow can accomplish motion estimation with flexibility similar to pure optical flow, but at a fraction of the computational expense. Control grid methods also offer the added benefit of representing motion far more compactly than pure optical flow. This booklet explores control grid motion estimation and provides implementations of the approach that apply to data of multiple dimensionalities. Important current applications of control grid methods including registration and interpolation are also developed. Table of Contents: Introduction / Control Grid Interpolation (CGI) / Application of CGI to Registration Problems / Application of CGI to Interpolation Problems / Discussion and Conclusions

Eye-movements aid the control of locomotion.

Eye-movements have long been considered a problem when trying to understand the visual control of locomotion. They transform the retinal image from a simple expanding pattern of moving texture elements (pure optic flow), into a complex combination of translation and rotation components (retinal flow). In this article we investigate whether there are measurable advantages to having an active free gaze, over a static gaze or tracking gaze, when steering along a winding path. We also examine patterns of free gaze behavior to determine preferred gaze strategies during active locomotion. Participants were asked to steer along a computer-simulated textured roadway with free gaze, fixed gaze, or gaze tracking the center of the roadway. Deviation of position from the center of the road was recorded along with their point of gaze. It was found that visually tracking the middle of the road produced smaller steering errors than for fixed gaze. Participants performed best at the steering task when allowed to sample naturally from the road ahead with free gaze. There was some variation in the gaze strategies used, but sampling was predominantly of areas proximal to the center of the road. These results diverge from traditional models of flow analysis.

Absolute distance perception during in-depth head movement: calibrating optic flow with extra-retinal information

We investigated the ability of monocular human observer to scale absolute distance during sagittal head motion in the presence of pure optic flow information. Subjects were presented at eye-level computer-generated spheres (covered with randomly distributed dots) placed at several distances. We compared the condition of self-motion (SM) versus object-motion (OM) using equivalent optic flow field. When the amplitude of head movement was relatively constant, subjects estimated absolute distance rather accurately in both the SM and OM conditions. However, when the amplitude changed on a trial-to-trial basis, subjects' performance deteriorated only in the OM condition. We found that distance judgment in OM condition correlated strongly with optic flow divergence, and that non-visual cues served as important factors for scaling distances in SM condition. Absolute distance also seemed to be better scaled with sagittal head movement when compared with lateral head translation.