4 research outputs found
Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1
Previous studies in that line suggested that lateral interactions of V1 cells
are responsible, among other visual effects, of bottom-up visual attention
(alternatively named visual salience or saliency). Our objective is to mimic
these connections in the visual system with a neurodynamic network of
firing-rate neurons. Early subcortical processes (i.e. retinal and thalamic)
are functionally simulated. An implementation of the cortical magnification
function is included to define the retinotopical projections towards V1,
processing neuronal activity for each distinct view during scene observation.
Novel computational definitions of top-down inhibition (in terms of inhibition
of return and selection mechanisms), are also proposed to predict attention in
Free-Viewing and Visual Search conditions. Results show that our model
outpeforms other biologically-inpired models of saliency prediction as well as
to predict visual saccade sequences during free viewing. We also show how
temporal and spatial characteristics of inhibition of return can improve
prediction of saccades, as well as how distinct search strategies (in terms of
feature-selective or category-specific inhibition) predict attention at
distinct image contexts.Comment: 32 pages, 19 figure
Efficient neural models for visual attention
International audienceHuman vision rely on attention to select only a few regions to process and thus reduce the complexity and the processing time of visual task. Artificial vision systems can benefit from a bio-inspired attentional process relying on neural models. In such applications, what is the most efficient neural model: spiked-based or frequency-based? We propose an evaluation of both neural model, in term of complexity and quality of results (on artificial and natural images)