Chromatic Information and Feature Detection in Fast Visual Analysis

The visual system is able to recognize a scene based on a sketch made of very simple features. This ability is likely crucial for survival, when fast image recognition is necessary, and it is believed that a primal sketch is extracted very early in the visual processing. Such highly simplified representations can be sufficient for accurate object discrimination, but an open question is the role played by color in this process. Rich color information is available in natural scenes, yet artist's sketches are usually monochromatic; and, black-and-white movies provide compelling representations of real world scenes. Also, the contrast sensitivity of color is low at fine spatial scales. We approach the question from the perspective of optimal information processing by a system endowed with limited computational resources. We show that when such limitations are taken into account, the intrinsic statistical properties of natural scenes imply that the most effective strategy is to ignore fine-scale color features and devote most of the bandwidth to gray-scale information. We find confirmation of these information-based predictions from psychophysics measurements of fast-viewing discrimination of natural scenes. We conclude that the lack of colored features in our visual representation, and our overall low sensitivity to high-frequency color components, are a consequence of an adaptation process, optimizing the size and power consumption of our brain for the visual world we live in

Chromatic Information and Feature Detection in Fast Visual Analysis

The visual system is able to recognize a scene based on a sketch made of very simple features. This ability is likely crucial for survival, when fast image recognition is necessary, and it is believed that a primal sketch is extracted very early in the visual processing. Such highly simplified representations can be sufficient for accurate object discrimination, but an open question is the role played by color in this process. Rich color information is available in natural scenes, yet artist's sketches are usually monochromatic; and, black-and-white movies provide compelling representations of real world scenes. Also, the contrast sensitivity of color is low at fine spatial scales. We approach the question from the perspective of optimal information processing by a system endowed with limited computational resources. We show that when such limitations are taken into account, the intrinsic statistical properties of natural scenes imply that the most effective strategy is to ignore fine-scale color features and devote most of the bandwidth to gray-scale information. We find confirmation of these information-based predictions from psychophysics measurements of fast-viewing discrimination of natural scenes. We conclude that the lack of colored features in our visual representation, and our overall low sensitivity to high-frequency color components, are a consequence of an adaptation process, optimizing the size and power consumption of our brain for the visual world we live in

Florence “blues” are clothed in triple basic terms

Psycholinguistic studies provide evidence that Italian has more than one basic color term (BCT) for “blue”: consensually, blu denotes “dark blue,” while “light-and-medium blue,” with diatopic variation, is termed either azzurro or celeste. For Tuscan speakers (predominantly from Florence), the BLUE area is argued to linguistically differentiate between azzurro “medium blue” and celeste “light blue.” We scrutinized “basicness” of the three terms. Participants (N=31; university students/graduates born in Tuscany) named each chip of eight Munsell charts encompassing the BLUE area (5BG-5PB; N=237) using an unconstrained color-naming method. They then indicated the “best exemplar” (focal color) of blu, azzurro and celeste. We found that frequencies of the three terms and of term derivatives were comparable. Referential meaning of blu, azzurro, and celeste was estimated in CIELAB space as L∗a∗b∗-coordinates of the mean of focal colors and as “modal” categories, that is, dispersion around the mean. The three “blue” terms were distinct on both measures and separated along all three CIELAB dimensions but predominantly along the L∗-dimension. Our results provide evidence that Tuscan speakers require all three terms for naming the BLUE area, categorically refined along the lightness dimension. Furthermore, celeste appears to be athird BCT for “blue,” along with commonly considered BCTs azzurro and blu. The “triple blues” as BCTs for Tuscan speakers are in contrast with outcomes of two “blue” basic terms estimated by using the same methodology in two other locations in Italy—azzurro and blu (Verona, Veneto region) or celeste and blu (Alghero, Sardinia)

Inversion of perceived direction of motion caused by spatial undersampling in two children with periventricular leukomalacia

We report here two cases of two young diplegic patients with cystic periventricular leukomalacia who systematically, and with high sensitivity, perceive translational motion of a random-dot display in the opposite direction. The apparent inversion was specific for translation motion: Rotation and expansion motion were perceived correctly, with normal sensitivity. It was also specific for random-dot patterns, not occurring with gratings. For the one patient that we were able to test extensively, contrast sensitivity for static stimuli was normal, but was very low for direction discrimination at high spatial frequencies and all temporal frequencies. His optokinetic nystagmus movements were normal but he was unable to track a single translating target, indicating a perceptual origin of the tracking deficit. The severe deficit for motion perception was also evident in the seminatural situation of a driving simulation video game. The perceptual deficit for translational motion was reinforced by functional magnetic resonance imaging studies. Translational motion elicited no response in the MT complex, although it did produce a strong response in many visual areas when contrasted with blank stimuli. However, radial and rotational motion produced a normal pattern of activation in a subregion of the MT complex. These data reinforce the existent evidence for independent cortical processing for translational, and circular or radial flow motion, and further suggest that the two systems have different vulnerability and plasticity to prenatal damage. They also highlight the complexity of visual motion perception, and how the delicate balance of neural activity can lead to paradoxical effects such as consistent misperception of the direction of motion. We advance a possible explanation of a reduced spatial sampling of the motion stimuli and report a simple model that simulates well the experimental results

Information and Perception of Meaningful Patterns

The visual system needs to extract the most important elements of the external world from a large flux of information in a short time for survival purposes. It is widely believed that in performing this task, it operates a strong data reduction at an early stage, by creating a compact summary of relevant information that can be handled by further levels of processing. In this work we formulate a model of early vision based on a pattern-filtering architecture, partly inspired by high-speed digital data reduction in experimental high-energy physics (HEP). This allows a much stronger data reduction than models based just on redundancy reduction. We show that optimizing this model for best information preservation under tight constraints on computational resources yields surprisingly specific a-priori predictions for the shape of biologically plausible features, and for experimental observations on fast extraction of salient visual features by human observers. Interestingly, applying the same optimized model to HEP data acquisition systems based on pattern-filtering architectures leads to specific a-priori predictions for the relevant data patterns that these devices extract from their inputs. These results suggest that the limitedness of computing resources can play an important role in shaping the nature of perception, by determining what is perceived as “meaningful features” in the input data.</p

Monte Carlo simulation of track reconstruction and pattern filtering in a HEP particle detector.

<p><b>a</b>, Schematic representation of a sector of a four-layers tracking detector, with simulated data (see Methods). Black dots represent measured positions where flying particles cross the detector layers - they can also be produced by random noise. Each layer is subdivided into a finite number of intervals (<i>bins</i>), delimited here by vertical bars. Every possible combination of bins (one on each layer) defines a <i>pattern</i> (grey line example). Only a small fraction of the patterns are compatible with the presence of a real particle (red line example), <b>b</b>, Probability distribution of the frequency of patterns (<i>δ(p)</i>) produced by a sample of simulated events of the type shown in (a) (grey histogram). The distribution of the sub-sample of patterns corresponding to valid particle trajectories is shown as a red histogram. The red curve is the function of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069154#pone.0069154.e001" target="_blank">eq. 1</a>, with N = 50 and W = 0.15. The vertical red lines indicate the probability range selected by our model, using the constraint ∫_{<i>f(p)>c</i>}<i>pδ(p)</i>d<i>p.</i></p

Entropy yield per unit cost, plotted as a function of the pattern probability (eq. 1).

<p>Blue curve: limited bandwidth and unlimited pattern storage capacity (W = 0.001, N = ∞); green curve: limited storage and unlimited bandwidth (N = 100, W = ∞); Red curve: limited bandwidth and storage (N = 100, W = 0.001)). Parameter values and the vertical scale are arbitrarily chosen for illustration.</p

Human contrast sensitivity to visual patterns vs. model predictions.

<p><b>a,</b> Probability distribution of the 512 possible 3×3 1-bit pixel patterns (grey histogram). The curves are the model selection functions (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069154#pone.0069154.e001" target="_blank">eq. 1</a>) for W = 0.05 and two different values of N.(green: N = 50; blue: N = 15). Green and blue histograms are the probability distributions of corresponding selected patterns. Their actual bandwidth occupancies (∫_{<i>f(p)>c</i>}<i>pδ(p)</i>d<i>p</i>) turn out to be slightly lower (respectively 0.025 and 0.015) than the imposed limit W. Cyan and yellow histograms are the distributions of low-probability patterns used in our measurements. <b>b,c,</b> Visualization of the pattern sets shown in (a), in green and blue respectively. <b>d,</b> Visualization of the lowest-probability patterns (discarded by our approach due to large storage occupation). <b>e,</b> Visualization of the highest-probability patterns (discarded due to large bandwidth occupation). <b>f,</b> Averaged sensitivity for detection of the patterns as a function of their probability, measured on three human subjects (different colors). Errors are determined by the fit (see Methods). The results of pairwise statistical comparisons (z tests, N = 100) amongst sensitivities plotted in (f) are: </p

Examples of images from the database and sketches used.

<p><b>a</b> Examples of full color natural images extracted from the database <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069154#pone.0069154-Olmos1" target="_blank">[23]</a>, available at: <a href="http://tabby.vision.mcgill.ca/html/browsedownload.html" target="_blank">http://tabby.vision.mcgill.ca/html/browsedownload.html</a>. <b>b</b> Digitized versions of images in (a). <b>c</b> Sketches obtained from the images in (b), by using the optimal pattern set of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069154#pone-0069154-g003" target="_blank">fig. 3b</a>.</p