Measuring perceived differences in surface texture due to changes in higher order statistics

The Importance of Phase to Texture Discrimination and Similarity

In this article, we investigate the importance of phase for texture discrimination and similarity estimation tasks. We first use two psychophysical experiments to investigate the relative importance of phase and magnitude spectra for human texture discrimination and similarity estimation. The results show that phase is more important to humans for both tasks. We further examine the ability of 51 computational feature sets to perform these two tasks. In contrast with the psychophysical experiments, it is observed that the magnitude data is more important to these computational feature sets than the phase data. We hypothesise that this inconsistency is due to the difference between the abilities of humans and the computational feature sets to utilise phase data. This motivates us to investigate the application of the 51 feature sets to phase-only images in addition to their use on the original data set. This investigation is extended to exploit Convolutional Neural Network (CNN) features. The results show that our feature fusion scheme improves the average performance of those feature sets for estimating humans' perceptual texture similarity. The superior performance should be attributed to the importance of phase to texture similarity.

Perceptual Texture Similarity Estimation: An Evaluation of Computational Features

Estimation of texture similarity is fundamental to many material recognition tasks. This study uses fine-grained human perceptual similarity ground-truth to provide a comprehensive evaluation of 51 texture feature sets. We conduct two types of evaluation and both show that these features do not estimate similarity well when compared against human agreement rates, but that performances are improved when the features are combined using a Random Forest. Using a simple two-stage statistical model we show that few of the features capture long-range aperiodic relationships. We perform two psychophysical experiments which indicate that long-range interactions do provide humans with important cues for estimating texture similarity. This motivates an extension of the study to include Convolutional Neural Networks (CNNs) as they enable arbitrary features of large spatial extent to be learnt. Our conclusions derived from the use of two pre-trained CNNs are: that the large spatial extent exploited by the networks' top convolutional and first fully-connected layers, together with the use of large numbers of filters, confers significant advantage for estimation of perceptual texture similarity.

Flexible contextual modulation of naturalistic texture perception in peripheral vision

Peripheral vision comprises most of our visual field, and is essential in guiding visual behavior. Its characteristic capabilities and limitations, which distinguish it from foveal vision, have been explained by the most influential theory of peripheral vision as the product of representing the visual input using summary statistics. Despite its success, this account may provide a limited understanding of peripheral vision, because it neglects processes of perceptual grouping and segmentation. To test this hypothesis, we studied how contextual modulation, namely the modulation of the perception of a stimulus by its surrounds, interacts with segmentation in human peripheral vision. We used naturalistic textures, which are directly related to summary-statistics representations. We show that segmentation cues affect contextual modulation, and that this is not captured by our implementation of the summary-statistics model. We then characterize the effects of different texture statistics on contextual modulation, providing guidance for extending the model, as well as for probing neural mechanisms of peripheral vision.

Measuring and Modeling Visual Appearance

In studying visual perception, we seek to develop models of processing that accurately predict perceptual judgments. Much of this work is focused on judgments of discrimination, and there is a large literature concerning models of visual discrimination. There are, however, non-threshold visual judgments, such as judgments of the magnitude of differences between visual stimuli, that provide a means to bridge the gap between threshold and appearance. We describe two such models of suprathreshold judgments, maximum likelihood difference scaling and maximum likelihood conjoint measurement, and review recent literature that has exploited them.

Perceived regularity of a texture is influenced by the regularity of a surrounding texture

Previous studies have shown that texture regularity is adaptable, and have suggested that texture regularity might be coded by the peakedness of the underlying spatial frequency distribution. Here we demonstrate the related phenomenon of simultaneous regularity contrast (SRC), in which the perceived regularity of a central texture is influenced by the regularity of a surrounding texture. We presented center-surround arrangements of textures and measured the perceived regularity of the centre, using a centre-only comparison stimulus and a 2AFC procedure. From the resulting psychometric functions the SRC was measured as the difference between test and comparison regularity at the PSE (point of subjective equality). Observers generally exhibited asymmetric bidirectional SRC, in that more regular surrounds decreased the perceived regularity of the centre by between 20–40%, while less regular surrounds increased the perceived regularity of the centre by about 10%. Consistent with previous studies, a wavelet spatial frequency (SF) analysis of the stimuli revealed that their SF distributions became sharper with increased regularity, and therefore that distribution statistics such as kurtosis and SF bandwidth might be used to code regularity.

Determinants of neural responses to disparity in natural scenes

We studied disparity-evoked responses in natural scenes using high-density electroencephalography (EEG) in an event-related design. Thirty natural scenes that mainly included outdoor settings with trees and buildings were used. Twenty-four subjects viewed a series of trials composed of sequential two-alternative temporal forced-choice presentation of two different versions (two-dimensional [2D] vs. three-dimensional [3D]) of the same scene interleaved by a scrambled image with the same power spectrum. Scenes were viewed orthostereoscopically at 3 m through a pair of shutter glasses. After each trial, participants indicated with a key press which version of the scene was 3D. Performance on the discrimination was >90%. Participants who were more accurate also tended to respond faster; scenes that were reported more accurately as 3D also led to faster reaction times. We compared visual evoked potentials elicited by scrambled, 2D, and 3D scenes using reliable component analysis to reduce dimensionality. The disparity-evoked response to natural scene stimuli, measured from the difference potential between 2D and 3D scenes, comprised a sustained relative negativity in the dominant response component. The magnitude of the disparity-specific response was correlated with the observer's stereoacuity. Scenes with more homogeneous depth maps also tended to elicit large disparity-specific responses. Finally, the magnitude of the disparity-specific response was correlated with the magnitude of the differential response between scrambled and 2D scenes, suggesting that monocular higher-order scene statistics modulate disparity-specific responses.

Representation of Maximally Regular Textures in Human Visual Cortex

Naturalistic textures with an intermediate degree of statistical regularity can capture key structural features of natural images (Freeman and Simoncelli, 2011). V2 and later visual areas are sensitive to these features, while primary visual cortex is not (Freeman et al., 2013). Here we expand on this work by investigating a class of textures that have maximal formal regularity, the 17 crystallographic wallpaper groups (Fedorov, 1891). We used texture stimuli from four of the groups that differ in the maximum order of rotation symmetry they contain, and measured neural responses in human participants using functional MRI and high-density EEG. We found that cortical area V3 has a parametric representation of the rotation symmetries in the textures that is not present in either V1 or V2, the first discovery of a stimulus property that differentiates processing in V3 from that of lower-level areas. Parametric responses were also seen in higher-order ventral stream areas V4, VO1, and lateral occipital complex (LOC), but not in dorsal stream areas. The parametric response pattern was replicated in the EEG data, and source localization indicated that responses in V3 and V4 lead responses in LOC, which is consistent with a feedforward mechanism. Finally, we presented our stimuli to four well developed feedforward models and found that none of them were able to account for our results. Our results highlight structural regularity as an important stimulus dimension for distinguishing the early stages of visual processing, and suggest a previously unrecognized role for V3 in the visual form-processing hierarchy. Significance statement: Hierarchical processing is a fundamental organizing principle in visual neuroscience, with each successive processing stage being sensitive to increasingly complex stimulus properties. Here, we probe the encoding hierarchy in human visual cortex using a class of visual textures--wallpaper patterns--that are maximally regular. Through a combination of fMRI and EEG source imaging, we find specific responses to texture regularity that depend parametrically on the maximum order of rotation symmetry in the textures. These parametric responses are seen in several areas of the ventral visual processing stream, as well as in area V3, but not in V1 or V2. This is the first demonstration of a stimulus property that differentiates processing in V3 from that of lower-level visual areas.

Invariant texture perception is harder with synthetic textures: Implications for models of texture processing

Texture synthesis models have become a popular tool for studying the representations supporting texture processing in human vision. In particular, the summary statistics implemented in the Portilla-Simoncelli (P-S) model support high-quality synthesis of natural textures, account for performance in crowding and search tasks, and may account for the response properties of V2 neurons. We chose to investigate whether or not these summary statistics are also sufficient to support texture discrimination in a task that required illumination invariance. Our observers performed a match-to-sample task using natural textures photographed with either diffuse overhead lighting or lighting from the side. Following a briefly presented sample texture, participants identified which of two test images depicted the same texture. In the illumination change condition, illumination differed between the sample and the matching test image. In the no change condition, sample textures and matching test images were identical. Critically, we generated synthetic versions of these images using the P-S model and also tested participants with these. If the statistics in the P-S model are sufficient for invariant texture perception, performance with synthetic images should not differ from performance in the original task. Instead, we found a significant cost of applying texture synthesis in both lighting conditions. We also observed this effect when power-spectra were matched across images (Experiment 2) and when sample and test images were drawn from unique locations in the parent textures to minimize the contribution of image-based processing (Experiment 3). Invariant texture processing thus depends upon measurements not implemented in the P-S algorithm.

How sensitive is the human visual system to the local statistics of natural images?

A key hypothesis in sensory system neuroscience is that sensory representations are adapted to the statistical regularities in sensory signals and thereby incorporate knowledge about the outside world. Supporting this hypothesis, several probabilistic models of local natural image regularities have been proposed that reproduce neural response properties. Although many such physiological links have been made, these models have not been linked directly to visual sensitivity. Previous psychophysical studies of sensitivity to natural image regularities focus on global perception of large images, but much less is known about sensitivity to local natural image regularities. We present a new paradigm for controlled psychophysical studies of local natural image regularities and compare how well such models capture perceptually relevant image content. To produce stimuli with precise statistics, we start with a set of patches cut from natural images and alter their content to generate a matched set whose joint statistics are equally likely under a probabilistic natural image model. The task is forced choice to discriminate natural patches from model patches. The results show that human observers can learn to discriminate the higher-order regularities in natural images from those of model samples after very few exposures and that no current model is perfect for patches as small as 5 by 5 pixels or larger. Discrimination performance was accurately predicted by model likelihood, an information theoretic measure of model efficacy, indicating that the visual system possesses a surprisingly detailed knowledge of natural image higher-order correlations, much more so than current image models. We also perform three cue identification experiments to interpret how model features correspond to perceptually relevant image features.

Several aspects of primate visual physiology have been identified as adaptations to local regularities of natural images. However, much less work has measured visual sensitivity to local natural image regularities. Most previous work focuses on global perception of large images and shows that observers are more sensitive to visual information when image properties resemble those of natural images. In this work we measure human sensitivity to local natural image regularities using stimuli generated by patch-based probabilistic natural image models that have been related to primate visual physiology. We find that human observers can learn to discriminate the statistical regularities of natural image patches from those represented by current natural image models after very few exposures and that discriminability depends on the degree of regularities captured by the model. The quick learning we observed suggests that the human visual system is biased for processing natural images, even at very fine spatial scales, and that it has a surprisingly large knowledge of the regularities in natural images, at least in comparison to the state-of-the-art statistical models of natural images.

Contrast negation and texture synthesis differentially disrupt natural texture appearance

Natural textures have characteristic image statistics that make them discriminable from unnatural textures. For example, both contrast negation and texture synthesis alter the appearance of natural textures even though each manipulation preserves some features while disrupting others. Here, we examined the extent to which contrast negation and texture synthesis each introduce or remove critical perceptual features for discriminating unnatural textures from natural textures. We find that both manipulations remove information that observers use for distinguishing natural textures from transformed versions of the same patterns, but do so in different ways. Texture synthesis removes information that is relevant for discrimination in both abstract patterns and ecologically valid textures, and we also observe a category-dependent asymmetry for identifying an “oddball” real texture among synthetic distractors. Contrast negation exhibits no such asymmetry, and also does not impact discrimination performance in abstract patterns. We discuss our results in the context of the visual system’s tuning to ecologically relevant patterns and other results describing sensitivity to higher-order statistics in texture patterns.

Color and material perception: achievements and challenges

There is a large literature characterizing human perception of the lightness and color of matte surfaces arranged in coplanar arrays. In the past ten years researchers have begun to examine perception of lightness and color using wider ranges of stimuli intended to better approximate the conditions of everyday viewing. One emerging line of research concerns perception of lightness and color in scenes that approximate the three-dimensional environment we live in, with objects that need not be matte or coplanar and with geometrically complex illumination. A second concerns the perception of material surface properties other than color and lightness, such as gloss or roughness. This special issue features papers that address the rich set of questions and approaches that have emerged from these new research directions. Here, we briefly describe the articles in the issue and their relation to previous work.