Phase-Based Binocular Perception of Motion in Depth: Cortical-Like Operators and Analog VLSI Architectures

We present a cortical-like strategy to obtain reliable estimates of the motions of objects in a scene toward/away from the observer (motion in depth), from local measurements of binocular parameters derived from direct comparison of the results of monocular spatiotemporal filtering operations performed on stereo image pairs. This approach is suitable for a hardware implementation, in which such parameters can be gained via a feedforward computation (i.e., collection, comparison, and punctual operations) on the outputs of the nodes of recurrent VLSI lattice networks, performing local computations. These networks act as efficient computational structures for embedded analog filtering operations in smart vision sensors. Extensive simulations on both synthetic and real-world image sequences prove the validity of the approach that allows to gain high-level information about the 3D structure of the scene, directly from sensorial data, without resorting to explicit scene reconstruction

The AGILE Mission

AGILE is an Italian Space Agency mission dedicated to observing the gamma-ray Universe. The AGILE's very innovative instrumentation for the first time combines a gamma-ray imager (sensitive in the energy range 30 MeV-50 GeV), a hard X-ray imager (sensitive in the range 18-60 keV), a calorimeter (sensitive in the range 350 keV-100 MeV), and an anticoincidence system. AGILE was successfully launched on 2007 April 23 from the Indian base of Sriharikota and was inserted in an equatorial orbit with very low particle background. Aims. AGILE provides crucial data for the study of active galactic nuclei, gamma-ray bursts, pulsars, unidentified gamma-ray sources, galactic compact objects, supernova remnants, TeV sources, and fundamental physics by microsecond timing. Methods. An optimal sky angular positioning (reaching 0.1 degrees in gamma- rays and 1-2 arcmin in hard X-rays) and very large fields of view (2.5 sr and 1 sr, respectively) are obtained by the use of Silicon detectors integrated in a very compact instrument. Results. AGILE surveyed the gamma- ray sky and detected many Galactic and extragalactic sources during the first months of observations. Particular emphasis is given to multifrequency observation programs of extragalactic and galactic objects. Conclusions. AGILE is a successful high-energy gamma-ray mission that reached its nominal scientific performance. The AGILE Cycle-1 pointing program started on 2007 December 1, and is open to the international community through a Guest Observer Program

La struttura fisica della percezione: modelli e architetture di microsistemi percettivi

Dottorato di ricerca in ingegneria elettronica ed informatica. 8. ciclo. Relatore G. M. Bisio. Coordinatore R. ZoppoliConsiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome; Biblioteca Nazionale Centrale - P.za Cavalleggeri, 1, Florence / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Deep Representation Hierarchies for 3D Active Vision

Starting from the acknowledged properties of visual cortical neurons, we show how diversified and composite visual descriptors come up from different hierarchical combinations of the harmonic content of the visual signal. The resulting deep hierarchy networks can specialize to solve different tasks and trigger different behaviors, without necessarily getting through an explicit measure of the re-constructive visual attributes of the observed scene. Distinct specializations for stereopis and for active control of the vergence movements of a binocular system are presented. In particular, the advantage of not abandoning distributed representations of multiple solutions to prematurely construct integrated description of cognitive entities and commit the system to a particular behavior is discussed. Pilot CPU-GPU implementations of the proposed cortical-like architectures prove to be promising solutions for the next-generation of robot vision systems, which should be capable of calibrating and adapting autonomously through the interaction with the environment

Vergence control learning through real V1 disparity tuning curves

A 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

Emergence of oscillations and spatio-temporal coherence states in a continuum-model of excitatory and inhibitory neurons

A neural field model of the reaction-diffusion type for the emergence of oscillatory phenomena in visual cortices is proposed. To investigate the joint spatio-temporal oscillatory dynamics in a continuous distribution of excitatory and inhibitory neurons, the coupling among oscillators is modelled as a diffusion process, combined with non-linear point interactions. The model exhibits cooperative activation properties in both time and space, by reacting to volleys of activations at multiple cortical sites with ordered spatio-temporal oscillatory states, similar to those found in the physiological experiments on slow-wave field potentials. The possible use of the resulting spatial distributions of coherent states, as a flexible medium to establish feature association, is discussed

An integrated neuromimetic architecture for direct motion interpretation in the log-polar domain

A hierarchical vision system, inspired by the functional architecture of the cortical motion pathway, to provide motion interpretation and to guide real-time actions in the real-world, is proposed. Such a neuromimetic architecture exploits (i) log-polar mapping for data reduction, (ii) a population of motion energy neurons to compute the optic flow, and (iii) a population of adaptive templates in the cortical domain to gain the flow's affine description. The time-to-contact and the surface orientations of points of interest in the real-world are computed by directly combining the linear description of the cortical flow. The approach is validated through quantitative tests in synthetic environments, and in real-world automotive and robotics situations

A systematic analysis of a V1\u2013MT neural model for motion estimation

A neural feed-forward model composed of two layers that mimic the V1\u2013MT primary motion pathway, derived from previous works by Heeger and Simoncelli, is proposed and analyzed. Essential aspects of the model are highlighted and comparatively analyzed to point out how realistic neural responses can be efficiently and effectively used for optic flow estimation if properly combined at a population level. First, different profiles of the spatio-temporal V1 receptive fields are compared, both in terms of their properties in the frequency domain, and in terms of their responses to random dots and plaid stimuli. Then, a pooling stage at the MT level, which combines the afferent V1 responses, is modeled to obtain a population of pattern cells that encodes the local velocities of the visual stimuli. Finally, a decoding stage allows us to combine MT activities in order to compute optic flow. A systematic validation of the model is performed by computing the optic flow on synthetic and standard benchmark sequences with ground truth flow available. The average angular errors and the end-point errors on the resulting estimates allow us to quantitatively compare the different spatio-temporal profiles and the choices of the model\u5f3s parameters, and to assess the validity and effectiveness of the approach in realistic situations