19,465 research outputs found
Perception of global image contrast involves transparent spatial filtering and the integration and suppression of local contrasts (not RMS contrast)
When adjusting the contrast setting on a television set, we experience a perceptual change in the global image contrast. But how is that statistic computed? We addressed this using a contrast-matching task for checkerboard configurations of micro-patterns in which the contrasts and spatial spreads of two interdigitated components were controlled independently. When the patterns differed greatly in contrast, the higher contrast determined the perceived global contrast. Crucially, however, low contrast additions of one pattern to intermediate contrasts of the other caused a paradoxical reduction in the perceived global contrast. None of the following metrics/models predicted this: max, linear sum, average, energy, root mean squared (RMS), Legge and Foley. However, a nonlinear gain control model, derived from contrast detection and discrimination experiments, incorporating wide-field summation and suppression, did predict the results with no free parameters, but only when spatial filtering was removed. We conclude that our model describes fundamental processes in human contrast vision (the pattern of results was the same for expert and naive observers), but that above thresholdâ when contrast pedestals are clearly visibleâvisionâs spatial filtering characteristics become transparent, tending towards those of a delta function prior to spatial summation. The global contrast statistic from our model is as easily derived as the RMS contrast of an image, and since it more closely relates to human perception, we suggest it be used as an image contrast metric in practical applications
Online Mutual Foreground Segmentation for Multispectral Stereo Videos
The segmentation of video sequences into foreground and background regions is
a low-level process commonly used in video content analysis and smart
surveillance applications. Using a multispectral camera setup can improve this
process by providing more diverse data to help identify objects despite adverse
imaging conditions. The registration of several data sources is however not
trivial if the appearance of objects produced by each sensor differs
substantially. This problem is further complicated when parallax effects cannot
be ignored when using close-range stereo pairs. In this work, we present a new
method to simultaneously tackle multispectral segmentation and stereo
registration. Using an iterative procedure, we estimate the labeling result for
one problem using the provisional result of the other. Our approach is based on
the alternating minimization of two energy functions that are linked through
the use of dynamic priors. We rely on the integration of shape and appearance
cues to find proper multispectral correspondences, and to properly segment
objects in low contrast regions. We also formulate our model as a frame
processing pipeline using higher order terms to improve the temporal coherence
of our results. Our method is evaluated under different configurations on
multiple multispectral datasets, and our implementation is available online.Comment: Preprint accepted for publication in IJCV (December 2018
Blindfold learning of an accurate neural metric
The brain has no direct access to physical stimuli, but only to the spiking
activity evoked in sensory organs. It is unclear how the brain can structure
its representation of the world based on differences between those noisy,
correlated responses alone. Here we show how to build a distance map of
responses from the structure of the population activity of retinal ganglion
cells, allowing for the accurate discrimination of distinct visual stimuli from
the retinal response. We introduce the Temporal Restricted Boltzmann Machine to
learn the spatiotemporal structure of the population activity, and use this
model to define a distance between spike trains. We show that this metric
outperforms existing neural distances at discriminating pairs of stimuli that
are barely distinguishable. The proposed method provides a generic and
biologically plausible way to learn to associate similar stimuli based on their
spiking responses, without any other knowledge of these stimuli
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