1,354,362 research outputs found
The saccade main sequence revised: A fast and repeatable tool for oculomotor analysis
No full textSaccades are rapid ballistic eye movements that humans make to direct the fovea to an object of interest. Their kinematics is well defined, showing regular relationships between amplitude, duration, and velocity: the saccadic ’main sequence’. Deviations of eye movements from the main sequence can be used as markers of specific neurological disorders. Despite its significance, there is no general methodological consensus for reliable and repeatable measurements of the main sequence. In this work, we propose a novel approach for standard indicators of oculomotor performance. The obtained measurements are characterized by high repeatability, allowing for fine assessments of inter- and intra-subject variability, and inter-ocular differences. The designed experimental procedure is natural and non-fatiguing, thus it is well suited for fragile or non-collaborative subjects like neurological patients and infants. The method has been released as a software toolbox for public use. This framework lays the foundation for a normative dataset of healthy oculomotor performance for the assessment of oculomotor dysfunctions
Binocular eye movements are adapted to the natural environment
No full textHumans and many animals make frequent saccades requiring coordinated movements of the eyes. When landing on the new fixation point, the eyes must converge accurately or double images will be perceived. We asked whether the visual system uses statistical regularities in the natural environment to aid eye alignment at the end of saccades. We measured the distribution of naturally occurring disparities in different parts of the visual field. The central tendency of the distributions was crossed (nearer than fixation) in the lower field and uncrossed (farther) in the upper field in male and female participants. It was uncrossed in the left and right fields. We also measured horizontal vergence after completion of vertical, horizontal, and oblique saccades.Whenthe eyes first landed near the eccentric target, vergence was quite consistent with the natural-disparity distribution. For example, when making an upward saccade, the eyes diverged to be aligned with the most probable uncrossed disparity in that part of the visual field. Likewise, when making a downward saccade, the eyes converged to enable alignment with crossed disparity in that part of the field. Our results show that rapid binocular eye movements are adapted to the statistics of the 3D environment, minimizing the need for large corrective vergence movements at the end of saccades. The results are relevant to the debate about whether eye movements are derived from separate saccadic and vergence neural commands that control both eyes or from separate monocular commands that control the eyes independently
Vergence control learning through real V1 disparity tuning curves
No full textA neural network architecture able to autonomously learn effective disparity-vergence responses and drive the vergence eye movements of a simulated binocular active vision system is proposed. The proposed approach, instead of exploiting purposely designed resources, relies on the direct use of a set of real disparity tuning curves, measured in the primary visual cortex of two macaque monkeys and courteously made available by (Prince et al., 2002), that provides a distributed representation of binocular disparity. The network evolves following a differential Hebbian rule that exploits the overall population activity to measure the state of the system, i.e. The deviation from the desired vergence position, so as its modification as a consequence of the action performed. Accordingly, the signal provides an effective intrinsic reward to develop a stable and accurate vergence behaviour. Emerging from a direct interaction of the sensing system with the environment, the resulting control provides a precise and accurate control for small disparities, as well as a raw control on a broader working range when large disparities are experienced. The developed control converges to a stable state that intrinsically and continuously adapts to the characteristics of the ongoing stimulation. The results proved how actually naturally distributed resources allows for robust and flexible learning capabilities in changeable situations
Learning bio-inspired head-centric representations of 3D shapes in an active fixation setting
No full textWhen exploring the surrounding environment with the eyes, humans and primates need to interpret three-dimensional (3D) shapes in a fast and invariant way, exploiting a highly variant and gaze-dependent visual information. Since they have front-facing eyes, binocular disparity is a prominent cue for depth perception. Specifically, it serves as computational substrate for two ground mechanisms of binocular active vision: stereopsis and binocular coordination. To this aim, disparity information, which is expressed in a retinotopic reference frame, is combined along the visual cortical pathways with gaze information and transformed in a head-centric reference frame. Despite the importance of this mechanism, the underlying neural substrates still remain widely unknown. In this work, we investigate the capabilities of the human visual system to interpret the 3D scene exploiting disparity and gaze information. In a psychophysical experiment, human subjects were asked to judge the depth orientation of a planar surface either while fixating a target point or while freely exploring the surface. Moreover, we used the same stimuli to train a recurrent neural network to exploit the responses of a modelled population of cortical (V1) cells to interpret the 3D scene layout. The results for both human performance and from the model network show that integrating disparity information across gaze directions is crucial for a reliable and invariant interpretation of the 3D geometry of the scene
The Active Side of Stereopsis: Fixation Strategy and Adaptation to Natural Environments
No full textAbstractDepth perception in near viewing strongly relies on the interpretation of binocular retinal disparity to obtain stereopsis. Statistical regularities of retinal disparities have been claimed to greatly impact on the neural mechanisms that underlie binocular vision, both to facilitate perceptual decisions and to reduce computational load. In this paper, we designed a novel and unconventional approach in order to assess the role of fixation strategy in conditioning the statistics of retinal disparity. We integrated accurate realistic three-dimensional models of natural scenes with binocular eye movement recording, to obtain accurate ground-truth statistics of retinal disparity experienced by a subject in near viewing. Our results evidence how the organization of human binocular visual system is finely adapted to the disparity statistics characterizing actual fixations, thus revealing a novel role of the active fixation strategy over the binocular visual functionality. This suggests an ecological explanation for the intrinsic preference of stereopsis for a close central object surrounded by a far background, as an early binocular aspect of the figure-ground segregation process.</jats:p
Learning a Compositional Hierarchy of Disparity Descriptors for 3D Orientation Estimation in an Active Fixation Setting
No full textInteraction with everyday objects requires by the active visual system a fast and invariant reconstruction of their local shape layout, through a series of fast binocular fixation movements that change the gaze direction on the 3-dimensional surface of the object. Active binocular viewing results in complex disparity fields that, although informative about the orientation in depth (e.g., the slant and tilt), highly depend on the relative position of the eyes. Assuming to learn the statistical relationships between the differential properties of the disparity vector fields and the gaze directions, we expect to obtain more convenient, gaze-invariant visual descriptors. In this work, local approximations of disparity vector field differentials are combined in a hierarchical neural network that is trained to represent the slant and tilt from the disparity vector fields. Each gaze-related cell’s activation in the intermediate representation is recurrently merged with the other cells’ activations to gain the desired gaze-invariant selectivity. Although the representation has been tested on a limited set of combinations of slant and tilt, the resulting high classification rate validates the generalization capability of the approach
A Portable Bio-Inspired Architecture for Efficient Robotic Vergence Control
No full textIn stereoscopic vision, the ability of perceiving the three-dimensional structure of the surrounding environment is subordinated to a precise and effective motor control for the binocular coordination of the eyes/cameras. If, on the one side, the binocular coordination of camera movements is a complicating factor, on the other side, a proper vergence control, acting on the binocular disparity, facilitates the binocular fusion and the subsequent stereoscopic perception process. In real-world situations, an effective vergence control requires further features other than real time capabilities: real robot systems are indeed characterized by mechanical and geometrical imprecision that affect the binocular vision, and the illumination conditions are changeable and unpredictable. Moreover, in order to allow an effective visual exploration of the peripersonal space, it is necessary to cope with different gaze directions and provide a large working space. The proposed control strategy resorts to a neuromimetic approach that provides a distributed representation of disparity information. The vergence posture is obtained by an open-loop and a closed-loop control, which directly interacts with saccadic control. Before saccade, the open-loop component is computed in correspondence of the saccade target region, to obtain a vergence correction to be applied simultaneously with the saccade. At fixation, the closed-loop component drives the binocular disparity to zero in a foveal region. The obtained vergence servos are able to actively drive both the horizontal and the vertical alignment of the optical axes on the object of interest, thus ensuring a correct vergence posture. Experimental tests were purposely designed to measure the performance of the control in the peripersonal space, and were performed on three different robot platforms. The results demonstrated that the proposed approach yields real-time and effective vergence camera movements on a visual stimulus in a wide working range, regardless of the illumination in the environment and the geometry of the system
Solving Parallax Error for 3D Eye Tracking
No full textHead-mounted eye-Trackers allow for unrestricted behavior in the natural environment, but have calibration issues that compromise accuracy and usability. A well-known problem arises from the fact that gaze measurements suffer from parallax error due to the offset between the scene camera origin and eye position. To compensate for this error two pieces of data are required: The pose of the scene camera in head coordinates, and the three-dimensional coordinates of the fixation point in head coordinates. We implemented a method that allows for effective and accurate eye-Tracking in the three-dimensional environment. Our approach consists of a calibration procedure that allows to contextually calibrate the eye-Tracker and compute the eyes pose in the reference frame of the scene camera, and a custom stereoscopic scene camera that provides the three-dimensional coordinates of the fixation point. The resulting gaze data are free from parallax error, allowing accurate and effective use of the eye-Tracker in the natural environment
Autonomous learning of disparity-vergence behavior through distributed coding and population reward: Basic mechanisms and real-world conditioning on a robot stereo head
No full textAbstract A robotic system implementation that exhibits autonomous learning capabilities of effective control for vergence eye movements is presented. The system, directly relying on a distributed (i.e. neural) representation of binocular disparity, shows a large tolerance to the inaccuracies of real stereo heads and to the changeable environment. The proposed approach combines early binocular vision mechanisms with basic learning processes, such as synaptic plasticity and reward modulation. The computational substrate consists of a network of modeled V1 complex cells that act as oriented binocular disparity detectors. The resulting population response, besides implicit binocular depth cues about the environment, also provides a global signal (i.e. the overall activity of the population itself) to describe the state of the system and thus its deviation from the desired vergence position. The proposed network, by taking into account the modification of its internal state as a consequence of the action performed, evolves following a differential Hebbian rule. The overall activity of the population is exploited to derive an intrinsic signal that drives the weights update. Exploiting this signal implies a maximization of the population activity itself, thus providing an highly effective reward for the developing of a stable and accurate vergence behavior. The role of the different orientations in the learning process is evaluated separately against the whole population, evidencing that the interplay among the differently oriented channels allows a faster learning capability and a more accurate control. The efficacy of the proposed intrinsic reward signal is thus comparatively assessed against the ground-truth signal (the actual disparity) providing equivalent results, and thus validating the approach. Trained in a simulated environment, the proposed network, is able to cope with vergent geometry and thus to learn effective vergence movements for static and moving visual targets. Experimental tests with real robot stereo pairs demonstrate the capability of the architecture not just to directly learn from the environment, but to adapt the control to the stimulus characteristics
Effects of Guided Random Sampling of TCCs on Blood Flow Values in CT Perfusion Studies of Lung Tumors
No full textRationale and Objectives: Tissue perfusion is commonly used to evaluate lung tumor lesions through dynamic contrast-enhanced computed tomography (DCE-CT). The aim of this study was to improve the reliability of the blood flow (BF) maps by means of a guided sampling of the tissue time-concentration curves (TCCs). Materials and Methods: Fourteen selected CT perfusion (CTp) examinations from different patients with lung lesions were considered, according to different degrees of motion compensation. For each examination, two regions of interest (ROIs) referring to the target lesion and the arterial input were manually segmented. To obtain the perfusion parameters, we computed the maximum slope of the Hill equation, describing the pharmacokinetics of the contrast agent, and the TCC was fitted for each voxel. A guided iterative approach based on the Random Sample Consensus method was used to detect and exclude samples arising from motion artifacts through the assessment of the confidence level of each single temporal sample of the TCC compared to the model. Removing these samples permits to refine the model fitting, thus exploiting more reliable data. Goodness-of-fit measures of the fitted TCCs to the original data (eg, root mean square error and correlation distance) were used to assess the reliability of the BF values, so as to preserve the functional structure of the resulting perfusion map. We devised a quantitative index, the local coefficient of variation (lCV), to measure the spatial coherence of perfusion maps, from local to regional and global resolution. The effectiveness of the algorithm was tested under three different degrees of motion yielded by as many alignment procedures. Results: At pixel level, the proposed approach improved the reliability of BF values, quantitatively assessed through the correlation index. At ROI level, a comparative analysis emphasized how our approach "replaced" the noisy pixels, providing smoother parametric maps while preserving the main functional structure. Moreover, the implemented algorithm provides a more meaningful effect in correspondence of a higher motion degree. This was confirmed both quantitatively, using the lCV, and qualitatively, through visual inspection by expert radiologists. Conclusions: Perfusion maps achieved with the proposed approach can now be used as a valid tool supporting radiologists in DCE-CTp studies. This represents a step forward to clinical utilization of these studies for staging, prognosis, and monitoring values of therapeutic regimens
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