711 research outputs found
A closed curve is much more than an incomplete one: effect of closure in figure-ground segmentation.
Von Bezold assimilation effect reverses in stereoscopic conditions
Lightness contrast and lightness assimilation are opposite phenomena: in contrast,
grey targets appear darker when bordering bright surfaces (inducers) rather than dark ones; in
assimilation, the opposite occurs. The question is: which visual process favours the occurrence
of one phenomenon over the other? Researchers provided three answers to this question. The
first asserts that both phenomena are caused by peripheral processes; the second attributes their
occurrence to central processes; and the third claims that contrast involves central processes,
whilst assimilation involves peripheral ones. To test these hypotheses, an experiment on an IT
system equipped with goggles for stereo vision was run. Observers were asked to evaluate the
lightness of a grey target, and two variables were systematically manipulated: (i) the apparent
distance of the inducers; and (ii) brightness of the inducers. The retinal stimulation was kept
constant throughout, so that the peripheral processes remained the same. The results show that
the lightness of the target depends on both variables. As the retinal stimulation was kept constant, we
conclude that central mechanisms are involved in both lightness contrast and lightness assimilation
Symmetry lasts longer than random, but only for brief presentations.
Previous research has shown that explicit emotional content or physical image properties (e.g. luminance, size and numerosity) alter subjective duration. Palumbo et al. (2015) recently demonstrated that the presence or absence of abstract reflectional symmetry also influenced subjective duration. Here, we explored this phenomenon further by varying the type of symmetry (reflection or rotation) and the objective duration of stimulus presentation (less or more than one second). Experiment 1 used a verbal estimation task in which participants estimated the presentation duration of reflection, rotation symmetry or random square-field patterns. Longer estimates were given for reflectional symmetry images than rotation or random, but only when the image was presented for less than 1 second. There was no difference between rotation and random. These findings were confirmed by a second Experiment using a paired-comparison task. This temporal distortion could be because reflection has positive valence or because it is processed efficiently be the visual system. The mechanism remains to be determined. We are relatively sure, however, that reflectional patterns can increase subjective duration in the absence of explicit semantic content, and in the absence of changes in the size, luminance or numerosity in the images
Analysing Large Scale Structure: I. Weighted Scaling Indices and Constrained Randomisation
The method of constrained randomisation is applied to three-dimensional
simulated galaxy distributions. With this technique we generate for a given
data set surrogate data sets which have the same linear properties as the
original data whereas higher order or nonlinear correlations are not preserved.
The analysis of the original and surrogate data sets with measures, which are
sensitive to nonlinearities, yields information about the existence of
nonlinear correlations in the data. We demonstrate how to generate surrogate
data sets from a given point distribution, which have the same linear
properties (power spectrum) as well as the same density amplitude distribution.
We propose weighted scaling indices as a nonlinear statistical measure to
quantify local morphological elements in large scale structure. Using
surrogates is is shown that the data sets with the same 2-point correlation
functions have slightly different void probability functions and especially a
different set of weighted scaling indices. Thus a refined analysis of the large
scale structure becomes possible by calculating local scaling properties
whereby the method of constrained randomisation yields a vital tool for testing
the performance of statistical measures in terms of sensitivity to different
topological features and discriminative power.Comment: 10 pages, 5 figures, accepted for publication in MNRA
Hidden Cues in Random Line Stereograms
Successful fusion of random-line stereograms with breaks in the vernier acuity range has been interpreted to suggest that the interpolation process underlying hyperacuity is parallel and preliminary to stereomatching. In this paper (a) we demonstrate with computer experiments that vernier cues are not needed to solve the stereomatching problem posed by these stereograms and (b) we provide psychophysical evidence that human stereopsis probably does not use vernier cues alone to achieve fusion of these random-line stereograms.MIT Artificial Intelligence Laborator
Modulating attentional load affects numerosity estimation: evidence against a pre-attentive subitizing mechanism
Traditionally, the visual enumeration of a small number of items (1 to about 4), referred to as subitizing, has been thought of as a parallel and pre-attentive process and functionally different from the serial attentive enumeration of larger numerosities. We tested this hypothesis by employing a dual task paradigm that systematically manipulated the attentional resources available to an enumeration task. Enumeration accuracy for small numerosities was severely decreased as more attentional resources were taken away from the numerical task, challenging the traditionally held notion of subitizing as a pre-attentive, capacity-independent process. Judgement of larger numerosities was also affected by dual task conditions and attentional load. These results challenge the proposal that small numerosities are enumerated by a mechanism separate from large numerosities and support the idea of a single, attention-demanding enumeration mechanism
Multiscale Convolutional Neural Networks for Vision–Based Classification of Cells
International audienceWe present a Multiscale Convolutional Neural Network (MCNN) approach for vision-based classification of cells. Based on several deep Convolutional Neural Networks (CNN) acting at different resolutions, the proposed architecture avoid the classical handcrafted features extraction step, by processing features extraction and classification as a whole. The proposed approach gives better classification rates than classical state-of-the-art methods allowing a safer Computer-Aided Diagnosis of pleural cancer
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