92 research outputs found
Modeling Accommodation Control of the Human Eye: Chromatic Aberration and Color Opponency
No full textVergence 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
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
Evaluation of the Tobii EyeX Eye tracking controller and Matlab toolkit for research
No full textThe 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
Autonomous learning of disparity-vergence behaviour through distributed coding and population reward: Basic mechanisms and real-world conditioning on a robot stereo head
No full textA 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 behaviour. 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
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
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 GENUA PESTO Database - GENoa hUman Active fixation database: PEripersonal space STereoscopic images and grOund truth disparity
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