26,253 research outputs found
Predictive Coding as a Model of Biased Competition in Visual Attention
Attention acts, through cortical feedback pathways, to enhance the response of cells encoding expected or predicted information. Such observations are inconsistent with the predictive coding theory of cortical function which proposes that feedback acts to suppress information predicted by higher-level cortical regions. Despite this discrepancy, this article demonstrates that the predictive coding model can be used to simulate a number of the effects of attention. This is achieved via a simple mathematical rearrangement of the predictive coding model, which allows it to be interpreted as a form of biased competition model. Nonlinear extensions to the model are proposed that enable it to explain a wider range of data
Salient Object Detection via Augmented Hypotheses
In this paper, we propose using \textit{augmented hypotheses} which consider
objectness, foreground and compactness for salient object detection. Our
algorithm consists of four basic steps. First, our method generates the
objectness map via objectness hypotheses. Based on the objectness map, we
estimate the foreground margin and compute the corresponding foreground map
which prefers the foreground objects. From the objectness map and the
foreground map, the compactness map is formed to favor the compact objects. We
then derive a saliency measure that produces a pixel-accurate saliency map
which uniformly covers the objects of interest and consistently separates fore-
and background. We finally evaluate the proposed framework on two challenging
datasets, MSRA-1000 and iCoSeg. Our extensive experimental results show that
our method outperforms state-of-the-art approaches.Comment: IJCAI 2015 pape
Rapid Visual Categorization is not Guided by Early Salience-Based Selection
The current dominant visual processing paradigm in both human and machine
research is the feedforward, layered hierarchy of neural-like processing
elements. Within this paradigm, visual saliency is seen by many to have a
specific role, namely that of early selection. Early selection is thought to
enable very fast visual performance by limiting processing to only the most
salient candidate portions of an image. This strategy has led to a plethora of
saliency algorithms that have indeed improved processing time efficiency in
machine algorithms, which in turn have strengthened the suggestion that human
vision also employs a similar early selection strategy. However, at least one
set of critical tests of this idea has never been performed with respect to the
role of early selection in human vision. How would the best of the current
saliency models perform on the stimuli used by experimentalists who first
provided evidence for this visual processing paradigm? Would the algorithms
really provide correct candidate sub-images to enable fast categorization on
those same images? Do humans really need this early selection for their
impressive performance? Here, we report on a new series of tests of these
questions whose results suggest that it is quite unlikely that such an early
selection process has any role in human rapid visual categorization.Comment: 22 pages, 9 figure
Analog VLSI-Based Modeling of the Primate Oculomotor System
One way to understand a neurobiological system is by building a simulacrum that replicates its behavior in real time using similar constraints. Analog very large-scale integrated (VLSI) electronic circuit technology provides such an enabling technology. We here describe a neuromorphic system that is part of a long-term effort to understand the primate oculomotor system. It requires both fast sensory processing and fast motor control to interact with the world. A one-dimensional hardware model of the primate eye has been built that simulates the physical dynamics of the biological system. It is driven by two different analog VLSI chips, one mimicking cortical visual processing for target selection and tracking and another modeling brain stem circuits that drive the eye muscles. Our oculomotor plant demonstrates both smooth pursuit movements, driven by a retinal velocity error signal, and saccadic eye movements, controlled by retinal position error, and can reproduce several behavioral, stimulation, lesion, and adaptation experiments performed on primates
A feedback model of perceptual learning and categorisation
Top-down, feedback, influences are known to have significant effects on visual information processing. Such influences are also likely to affect perceptual learning. This article employs a computational model of the cortical region interactions underlying visual perception to investigate possible influences of top-down information on learning. The results suggest that feedback could bias the way in which perceptual stimuli are categorised and could also facilitate the learning of sub-ordinate level representations suitable for object identification and perceptual expertise
- …