41,063 research outputs found
Optimal measurement of visual motion across spatial and temporal scales
Sensory systems use limited resources to mediate the perception of a great
variety of objects and events. Here a normative framework is presented for
exploring how the problem of efficient allocation of resources can be solved in
visual perception. Starting with a basic property of every measurement,
captured by Gabor's uncertainty relation about the location and frequency
content of signals, prescriptions are developed for optimal allocation of
sensors for reliable perception of visual motion. This study reveals that a
large-scale characteristic of human vision (the spatiotemporal contrast
sensitivity function) is similar to the optimal prescription, and it suggests
that some previously puzzling phenomena of visual sensitivity, adaptation, and
perceptual organization have simple principled explanations.Comment: 28 pages, 10 figures, 2 appendices; in press in Favorskaya MN and
Jain LC (Eds), Computer Vision in Advanced Control Systems using Conventional
and Intelligent Paradigms, Intelligent Systems Reference Library,
Springer-Verlag, Berli
Neural Dynamics of Motion Processing and Speed Discrimination
A neural network model of visual motion perception and speed discrimination is presented. The model shows how a distributed population code of speed tuning, that realizes a size-speed correlation, can be derived from the simplest mechanisms whereby activations of multiple spatially short-range filters of different size are transformed into speed-tuned cell responses. These mechanisms use transient cell responses to moving stimuli, output thresholds that covary with filter size, and competition. These mechanisms are proposed to occur in the Vl→7 MT cortical processing stream. The model reproduces empirically derived speed discrimination curves and simulates data showing how visual speed perception and discrimination can be affected by stimulus contrast, duration, dot density and spatial frequency. Model motion mechanisms are analogous to mechanisms that have been used to model 3-D form and figure-ground perception. The model forms the front end of a larger motion processing system that has been used to simulate how global motion capture occurs, and how spatial attention is drawn to moving forms. It provides a computational foundation for an emerging neural theory of 3-D form and motion perception.Office of Naval Research (N00014-92-J-4015, N00014-91-J-4100, N00014-95-1-0657, N00014-95-1-0409, N00014-94-1-0597, N00014-95-1-0409); Air Force Office of Scientific Research (F49620-92-J-0499); National Science Foundation (IRI-90-00530
Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception
Choosing an appropriate set of stimuli is essential to characterize the
response of a sensory system to a particular functional dimension, such as the
eye movement following the motion of a visual scene. Here, we describe a
framework to generate random texture movies with controlled information
content, i.e., Motion Clouds. These stimuli are defined using a generative
model that is based on controlled experimental parametrization. We show that
Motion Clouds correspond to dense mixing of localized moving gratings with
random positions. Their global envelope is similar to natural-like stimulation
with an approximate full-field translation corresponding to a retinal slip. We
describe the construction of these stimuli mathematically and propose an
open-source Python-based implementation. Examples of the use of this framework
are shown. We also propose extensions to other modalities such as color vision,
touch, and audition
Engineering data compendium. Human perception and performance. User's guide
The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use
Integrated 2-D Optical Flow Sensor
I present a new focal-plane analog VLSI sensor that estimates optical flow in two visual dimensions. The chip significantly improves previous approaches both with respect to the applied model of optical flow estimation as well as the actual hardware implementation. Its distributed computational architecture consists of an array of locally connected motion units that collectively solve for the unique optimal optical flow estimate. The novel gradient-based motion model assumes visual motion to be translational, smooth and biased. The model guarantees that the estimation problem is computationally well-posed regardless of the visual input. Model parameters can be globally adjusted, leading to a rich output behavior. Varying the smoothness strength, for example, can provide a continuous spectrum of motion estimates, ranging from normal to global optical flow. Unlike approaches that rely on the explicit matching of brightness edges in space or time, the applied gradient-based model assures spatiotemporal continuity on visual information. The non-linear coupling of the individual motion units improves the resulting optical flow estimate because it reduces spatial smoothing across large velocity differences. Extended measurements of a 30x30 array prototype sensor under real-world conditions demonstrate the validity of the model and the robustness and functionality of the implementation
Computing motion in the primate's visual system
Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We will show how one well-known gradient-based computer algorithm for estimating visual motion can be implemented within the primate's visual system. This relaxation algorithm computes the optical flow field by minimizing a variational functional of a form commonly encountered in early vision, and is performed in two steps. In the first stage, local motion is computed, while in the second stage spatial integration occurs. Neurons in the second stage represent the optical flow field via a population-coding scheme, such that the vector sum of all neurons at each location codes for the direction and magnitude of the velocity at that location. The resulting network maps onto the magnocellular pathway of the primate visual system, in particular onto cells in the primary visual cortex (V1) as well as onto cells in the middle temporal area (MT). Our algorithm mimics a number of psychophysical phenomena and illusions (perception of coherent plaids, motion capture, motion coherence) as well as electrophysiological recordings. Thus, a single unifying principle ‘the final optical flow should be as smooth as possible’ (except at isolated motion discontinuities) explains a large number of phenomena and links single-cell behavior with perception and computational theory
State Dependence of Stimulus-Induced Variability Tuning in Macaque MT
Behavioral states marked by varying levels of arousal and attention modulate
some properties of cortical responses (e.g. average firing rates or pairwise
correlations), yet it is not fully understood what drives these response
changes and how they might affect downstream stimulus decoding. Here we show
that changes in state modulate the tuning of response variance-to-mean ratios
(Fano factors) in a fashion that is neither predicted by a Poisson spiking
model nor changes in the mean firing rate, with a substantial effect on
stimulus discriminability. We recorded motion-sensitive neurons in middle
temporal cortex (MT) in two states: alert fixation and light, opioid
anesthesia. Anesthesia tended to lower average spike counts, without decreasing
trial-to-trial variability compared to the alert state. Under anesthesia,
within-trial fluctuations in excitability were correlated over longer time
scales compared to the alert state, creating supra-Poisson Fano factors. In
contrast, alert-state MT neurons have higher mean firing rates and largely
sub-Poisson variability that is stimulus-dependent and cannot be explained by
firing rate differences alone. The absence of such stimulus-induced variability
tuning in the anesthetized state suggests different sources of variability
between states. A simple model explains state-dependent shifts in the
distribution of observed Fano factors via a suppression in the variance of gain
fluctuations in the alert state. A population model with stimulus-induced
variability tuning and behaviorally constrained information-limiting
correlations explores the potential enhancement in stimulus discriminability by
the cortical population in the alert state.Comment: 36 pages, 18 figure
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