1,720,998 research outputs found
The saccade main sequence revised: A fast and repeatable tool for oculomotor analysis
Full text linkSaccades 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
Full text linkHumans 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
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
Learning bio-inspired head-centric representations of 3D shapes in an active fixation setting
Full text linkWhen 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
An integrated system based on binocular learned receptive fields for saccade-vergence on visually salient targets
No full textThe human visual system uses saccadic and vergence eyes movements to foveate interesting objects with both eyes, and thus exploring the visual scene. To mimic this biological behavior in active vision, we proposed a bio-inspired integrated system able to learn a functional sensory representation of the environment, together with the motor commands for binocular eye coordination, directly by interacting with the environment itself. The proposed architecture, rather than sequentially combining different functionalities, is a robust integration of different modules that rely on a front-end of learned binocular receptive fields to specialize on different sub-Tasks. The resulting modular architecture is able to detect salient targets in the scene and perform precise binocular saccadic and vergence movement on it. The performances of the proposed approach has been tested on the iCub Simulator, providing a quantitative evaluation of the computational potentiality of the learned sensory and motor resources
Evaluation of the Tobii EyeX Eye tracking controller and Matlab toolkit for research
Full text linkThe Tobii Eyex Controller is a new low-cost binocular eye tracker marketed for integration in gaming and consumer applications. The manufacturers claim that the system was conceived for natural eye gaze interaction, does not require continuous recalibration, and allows moderate head movements. The Controller is provided with a SDK to foster the development of new eye tracking applications. We review the characteristics of the device for its possible use in scientific research. We develop and evaluate an open source Matlab Toolkit that can be employed to interface with the EyeX device for gaze recording in behavioral experiments. The Toolkit provides calibration procedures tailored to both binocular and monocular experiments, as well as procedures to evaluate other eye tracking devices. The observed performance of the EyeX (i.e. accuracy < 0.6°, precision < 0.25°, latency < 50 ms and sampling frequency ≈55 Hz), is sufficient for some classes of research application. The device can be successfully employed to measure fixation parameters, saccadic, smooth pursuit and vergence eye movements. However, the relatively low sampling rate and moderate precision limit the suitability of the EyeX for monitoring micro-saccadic eye movements or for real-time gaze-contingent stimulus control. For these applications, research grade, high-cost eye tracking technology may still be necessary. Therefore, despite its limitations with respect to high-end devices, the EyeX has the potential to further the dissemination of eye tracking technology to a broad audience, and could be a valuable asset in consumer and gaming applications as well as a subset of basic and clinical research settings
The blur horopter: Retinal conjugate surface in binocular viewing
Full text linkFrom measurements of wavefront aberrations in 16 emmetropic eyes, we calculated where objects in the world create best-focused images across the central 27◦ (diameter) of the retina. This is the retinal conjugate surface.We calculated how the surface changes as the eye accommodates from near to far and found that it mostly maintains its shape. The conjugate surface is pitched top-back, meaning that the upper visual field is relatively hyperopic compared to the lower field.We extended the measurements of best image quality into the binocular domain by considering how the retinal conjugate surfaces for the two eyes overlap in binocular viewing. We call this binocular extension the blur horopter.We show that in combining the two images with possibly different sharpness, the visual system creates a larger depth of field of apparently sharp images than occurs with monocular viewing. We examined similarities between the blur horopter and its analog in binocular vision: the binocular horopter.We compared these horopters to the statistics of the natural visual environment. The binocular horopter and scene statistics are strikingly similar. The blur horopter and natural statistics are qualitatively, but not quantitatively, similar. Finally, we used the measurements to refine what is commonly referred to as the zone of clear single binocular vision
Autonomous learning of disparity-vergence behavior through distributed coding and population reward: Basic mechanisms and real-world conditioning on a robot stereo head
Full text linkAbstract 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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