2,839 research outputs found
Automated Design of Salient Object Detection Algorithms with Brain Programming
Despite recent improvements in computer vision, artificial visual systems'
design is still daunting since an explanation of visual computing algorithms
remains elusive. Salient object detection is one problem that is still open due
to the difficulty of understanding the brain's inner workings. Progress on this
research area follows the traditional path of hand-made designs using
neuroscience knowledge. In recent years two different approaches based on
genetic programming appear to enhance their technique. One follows the idea of
combining previous hand-made methods through genetic programming and fuzzy
logic. The other approach consists of improving the inner computational
structures of basic hand-made models through artificial evolution. This
research work proposes expanding the artificial dorsal stream using a recent
proposal to solve salient object detection problems. This approach uses the
benefits of the two main aspects of this research area: fixation prediction and
detection of salient objects. We decided to apply the fusion of visual saliency
and image segmentation algorithms as a template. The proposed methodology
discovers several critical structures in the template through artificial
evolution. We present results on a benchmark designed by experts with
outstanding results in comparison with the state-of-the-art.Comment: 35 pages, 5 figure
A computational dynamical model of human visual cortex for visual search and feature-based attention
Visual attention can be deployed to locations within the visual array (spatial attention), to individual features such as colour and form (feature-based attention), or to
entire objects (object-based attention). Objects are composed of features to form a perceived ‘whole’. This compositional object representation reduces the storage
demands by avoiding the need to store every type of object experienced. However, this approach exposes a problem of binding these constituent features (e.g. form and colour) into objects. The problem is made explicit in the higher areas of the ventral stream as information about a feature’s location is absent. For feature-based attention and search, activations flow from the inferotemporal cortex to primary visual cortex without spatial cues from the dorsal stream, therefore the neural effect is applied to all locations across the visual field [79, 60, 7, 52].
My research hypothesis is that biased competition occurs independently for each cued feature, and is implemented by lateral inhibition between a feedforward and a feedback network through a cortical micro-circuit architecture. The local competition for each feature can be combined in the dorsal stream via spatial congruence to implement a secondary spatial attention mechanism, and in early visual areas to bind together the distributed featural representation of a target object
The neuroscience of vision-based grasping: a functional review for computational modeling and bio-inspired robotics
The topic of vision-based grasping is being widely studied using various techniques and
with different goals in humans and in other primates. The fundamental related findings are
reviewed in this paper, with the aim of providing researchers from different fields, including
intelligent robotics and neural computation, a comprehensive but accessible view on the
subject. A detailed description of the principal sensorimotor processes and the brain areas
involved in them is provided following a functional perspective, in order to make this survey
especially useful for computational modeling and bio-inspired robotic application
Change blindness: eradication of gestalt strategies
Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task
Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks
Research at the intersection of computer vision and neuroscience has revealed hierarchical correspondence between layers of deep convolutional neural networks (DCNNs) and cascade of regions along human ventral visual cortex. Recently, studies have uncovered emergence of human interpretable concepts within DCNNs layers trained to identify visual objects and scenes. Here, we asked whether an artificial neural network (with convolutional structure) trained for visual categorization would demonstrate spatial correspondences with human brain regions showing central/peripheral biases. Using representational similarity analysis, we compared activations of convolutional layers of a DCNN trained for object and scene categorization with neural representations in human brain visual regions. Results reveal a brain-like topographical organization in the layers of the DCNN, such that activations of layer-units with central-bias were associated with brain regions with foveal tendencies (e.g. fusiform gyrus), and activations of layer-units with selectivity for image backgrounds were associated with cortical regions showing peripheral preference (e.g. parahippocampal cortex). The emergence of a categorical topographical correspondence between DCNNs and brain regions suggests these models are a good approximation of the perceptual representation generated by biological neural networks
- …