1,721,124 research outputs found
Graphics processing unit-accelerated techniques for bio-inspired computation in the primary visual cortex
No full textThe spread of graphics processing unit (GPU) computing paved the way to the possibility of reaching high-computing performances in the simulation of complex biological systems. In this work, we develop a very efficient GPU-accelerated neural library, which can be employed in real-world contexts. Such a library provides the neural functionalities that are the basis of a wide range of bio-inspired models, and in particular, we show its efficacy in implementing a cortical-like architecture for visual feature coding and estimation. In order to fully exploit the intrinsic parallelism of such neural architectures and to manage the huge amount of data that characterizes the internal representation of distributed neural models, we devise an effective algorithmic solution and an efficient data structure. In particular, we exploit both data parallelism and task parallelism, with the aim of optimally taking advantage from the computational capabilities of modern graphics cards. Moreover, we assess the performances of two different development frameworks, both supplying a wide range of basic signal processing GPU-accelerated functions. A systematic analysis, aiming at comparing different algorithmic solutions, shows the best data structure and parallelization computational scheme to compute features from a distributed population of neural units
A Computational Model for the Neural Representation and Estimation of the Binocular Vector Disparity from Convergent Stereo Image Pairs
No full textThe depth cue is a fundamental piece of information for artificial and living beings who interact with the surrounding environment in order to handle objects and to avoid obstacles: in such situations, the disparity patterns, which arise when agents fixate objects, are vector fields. We propose a biologically-inspired computational model to estimate dense horizontal and vertical disparity maps by exploiting the cortical paradigms of the primate visual system: in particular, we aim to model the disparity sensitivity of the V1-MT visual pathway. The proposed model is based on a first processing stage composed of a bank of spatial band-pass filters and a static nonlinearity, mimicking complex binocular cells. Then, subsequent pooling stages and decoding strategies allow the model to estimate the vector disparity, after having represented it as a population of MT-like units. We assess the proposed model by using standard benchmarking stereo images, the Middlebury dataset, and specific stereo images that have horizontal and vertical disparities, which characterize the stimuli produced by active vision systems. Moreover, we systemically analyze how the different processing stages affect the model performance, and we discuss their implications for the neural modeling
Enhanced Log-Polar implementation of Blind-Spot model
No full textSviluppo di un simulatore del modello spazio variante di acquisizione di immagini "Log-polar Blind
Spot Model" presentato nel lavoro in conferenza di M. Chessa, S.P. Sabatini, F. Solari, F. Tatti
dal titolo "A Quantitative Comparison of Speed and Reliability for Log-Polar Mapping
Techniques". Tale simulatore (realizzato da M. Chessa e F. Solari) e` disponibile nella libreria di
Computer Vision OpenCV (http://opencv.org/).Enhanced Log-Polar implementation (that uses Blind-Spot model) has been contributed by Fabio Solari and Manuela Chessa, see http://code.opencv.org/projects/opencv/wiki/2012 ( opencv/contrib/contrib.hpp, LogPolar_* classes and opencv/samples/cpp/logpolar_bsm.cpp sample)
FFV1MT: A V1-MT feedforward architecture for optical flow estimation
No full textA neural feed-forward model composed of two layers that mimic the V1-MT primary motion pathway, derived from previous works by Heeger and Simoncelli. Reference: Solari F, Chessa M, Medathati NVK, Kornprobst P (2015) What can we expect from a V1-MT feedforward architecture for optical flow estimation? Signal Processing: Image Communicatio
A virtual reality simulator for active stereo vision systems
No full textThe virtual reality is a powerful tool to simulate the behavior of the physical systems. The visual system of a robot and its interplay with the 3D environment can be modeled and simulated through the geometrical relationships between the virtual stereo cameras and the virtual 3D world. The novelty of our approach is related to the use of the virtual reality as a tool to simulate the behavior of active vision systems. In the standard way, the virtual reality is used for the perceptual rendering of the visual information exploitable by a human user. In the proposed approach, a virtual world is rendered to simulate the actual projections on the cameras of a robotic system, thus the mechanisms of the active vision are quantitatively validated by using the available ground truth data
Studying natural human-computer interaction in immersive virtual reality: A comparison between actions in the peripersonal and in the near-action space
No full textInteracting in immersive virtual reality is a challenging and open issue in human-computer interaction. Here, we describe a system to evaluate the performance of a low-cost setup, which has not the need of wearing devices to manipulate virtual objects. In particular, we consider the Leap Motion device and we assess its performance into two situations: reaching and grasp in the peripersonal space, and in the near-action space, i.e. when a user stays on foot and can move his own arms to reach objects on a desk. We show how these two situations are similar in terms of user performance, thus indicating a possible use of such device in a wide range of reaching tasks in immersive virtual reality
A systematic analysis of a V1–MT neural model for motion estimation
No full textA neural feed-forward model composed of two layers that mimic the V1–MT 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׳s parameters, and to assess the validity and effectiveness of the approach in realistic situations
Metodo basato sulla sfocatura inversa per dispositivi multimediali immersivi per attenuare il conflitto vergenza/accomodamento
No full textL'invenzione è un sistema per mitigare il conflitto vergenza-accomodazione dei visori multimediali immersivi. Si usa la sfocatura inversa (deconvoluzione di Wiener) anche con una nuova tecnica spazio variante per alterare le immagini stereo presentate all'utente. Può essere perfettamente integrato nei dispositivi commerciali migliorando la percezione della profondità e rendendoli più accessibili
Adjustable linear models for optic flow based obstacle avoidance
No full textAn original framework to recover the first-order spatial description of the optic flow is proposed. The approach is based on recursive filtering, and uses a set of linear models that dynamically adjust their properties on the basis of context information. These models are inspired by the experimental evidence about motion analysis in biological systems. By checking the presence of these models in the optic flow through a multiple model Kalman Filter, it is possible to compute the coefficients of the affine description and to use this information for estimating the motion of the observer as well as the three-dimensional orientation of the surfaces in some points of interest in the scene. In order to systematically validate the approach, a set of benchmarking sequences is used, and, finally, the proposed algorithm is successfully applied in real-world automotive situations
- …
