The relation of phase noise and luminance contrast to overt attention in complex visual stimuli

Models of attention are typically based on difference maps in low-level features but neglect higher order stimulus structure. To what extent does higher order statistics affect human attention in natural stimuli? We recorded eye movements while observers viewed unmodified and modified images of natural scenes. Modifications included contrast modulations (resulting in changes to first- and second-order statistics), as well as the addition of noise to the Fourier phase (resulting in changes to higher order statistics). We have the following findings: (1) Subjects' interpretation of a stimulus as a “natural” depiction of an outdoor scene depends on higher order statistics in a highly nonlinear, categorical fashion. (2) Confirming previous findings, contrast is elevated at fixated locations for a variety of stimulus categories. In addition, we find that the size of this elevation depends on higher order statistics and reduces with increasing phase noise. (3) Global modulations of contrast bias eye position toward high contrasts, consistent with a linear effect of contrast on fixation probability. This bias is independent of phase noise. (4) Small patches of locally decreased contrast repel eye position less than large patches of the same aggregate area, irrespective of phase noise. Our findings provide evidence that deviations from surrounding statistics, rather than contrast per se, underlie the well-established relation of contrast to fixation

Extensions of independent component analysis for natural image data

An understanding of the statistical properties of natural images is useful for any kind of processing to be performed on them. Natural image statistics are, however, in many ways as complex as the world which they depict. Fortunately, the dominant low-level statistics of images are sufficient for many different image processing goals. A lot of research has been devoted to second order statistics of natural images over the years. Independent component analysis is a statistical tool for analyzing higher than second order statistics of data sets. It attempts to describe the observed data as a linear combination of independent, latent sources. Despite its simplicity, it has provided valuable insights of many types of natural data. With natural image data, it gives a sparse basis useful for efficient description of the data. Connections between this description and early mammalian visual processing have been noticed. The main focus of this work is to extend the known results of applying independent component analysis on natural images. We explore different imaging techniques, develop algorithms for overcomplete cases, and study the dependencies between the components by using a model that finds a topographic ordering for the components as well as by conditioning the statistics of a component on the activity of another. An overview is provided of the associated problem field, and it is discussed how these relatively small results may eventually be a part of a more complete solution to the problem of vision.reviewe

The statistics of contour fragments in natural scenes

Summary: Recent electrophysiology recordings in macaque V4/IT suggest that single neuron response to synthetic closed contours can be largely captured by models which only consider a small number of contour fragments (Brincat and Connor 2004). Motivated by this experimental work, we sought firstly to characterize the statistics of contour fragments in natural scenes, and secondly to generate synthetic images which reflect the measured contour-fragment statistics.

To detect contour fragments, we defined a set of feature detectors which respond only in the presence of two edges co-occurring at a fixed relative angle – implemented as a logical ‘AND’ of two Gabor-like, laplacian-of-gaussian linear filters. We then determined the pairwise correlations of these contour fragments in a natural image ensemble. If efficient coding extends to higher cortical centers and processing in the ventral visual stream can be modeled as a sequence of logical operations on linear shape features, then the pairwise statistics we measure should be informative about neural shape coding. 

Using these statistics directly, it is possible to produce a generative model of simple images which contain the measured statistics. We implemented a modified Ising model and solved the inverse problem of determining the optimal model parameters which satisfy the measured correlations. The resulting Ising-like model of the pairwise statistics can generate the probability of any arrangement of contour fragments as measured in the natural image ensemble. 

As a complementary approach to producing images with naturalistic contour fragment statistics, it is possible to start with a natural scene and isolate the target features. This is achieved by applying our contour fragment detection processing to the single scene and then separately visualizing the fragments detected. This second procedure lends itself to parametric randomization of the generated image.

Narrative Elaboration: The central question guiding our study is how shapes are represented in inferotemporal cortex. To that end, we have investigated natural images in order to motivate experiments capable of targeting the extent to which neural processing of shapes involves representing shapes as combinations of key contour features. To simplify, we are focusing on black-and-white images and prioritizing contour features. This project suggests it is possible to generate synthetic images containing only a select set of contour statistics. Our subsequent goals include conducting collaborative macaque electrophysiology experiments with our generated images as visual stimuli

Modeling the Possible Influences of Eye Movements on the Refinement of Cortical Direction Selectivity

The second-order statistics of neural activity was examined in a model of the cat LGN and V1 during free-viewing of natural images. In the model, the specific patterns of thalamocortical activity required for a Bebbian maturation of direction-selective cells in VI were found during the periods of visual fixation, when small eye movements occurred, but not when natural images were examined in the absence of fixational eye movements. In addition, simulations of stroboscopic reming that replicated the abnormal pattern of eye movements observed in kittens chronically exposed to stroboscopic illumination produced results consistent with the reported loss of direction selectivity and preservation of orientation selectivity. These results suggest the involvement of the oculomotor activity of visual fixation in the maturation of cortical direction selectivity

The human visual system is optimised for processing the spatial information in natural visual images

AbstractA fundamental tenet of visual science is that the detailed properties of visual systems are not capricious accidents, but are closely matched by evolution and neonatal experience to the environments and lifestyles in which those visual systems must work [1–5]. This has been shown most convincingly for fish [6] and insects [7]. For mammalian vision, however, this tenet is based more upon theoretical arguments [8–11] than upon direct observations [12,13]. Here, we describe experiments that require human observers to discriminate between pictures of slightly different faces or objects. These are produced by a morphing technique that allows small, quantifiable changes to be made in the stimulus images. The independent variable is designed to give increasing deviation from natural visual scenes, and is a measure of the Fourier composition of the image (its second-order statistics). Performance in these tests was best when the pictures had natural second-order spatial statistics, and degraded when the images were made less natural. Furthermore, performance can be explained with a simple model of contrast coding, based upon the properties of simple cells [14–17] in the mammalian visual cortex. The findings thus provide direct empirical support for the notion that human spatial vision is optimised to the second-order statistics of the optical environment

Natural Image Statistics for Natural Image Segmentation

Building on recent progress in modeling filter response statistics of natural mages we integrate a statistical model into a variational framework for image segmentation. Incorporated in asound probabilistic distance measure the model drives level sets toward meaningful segment at ions of complex textures and natural scenes. Despite its enhanced descriptive power our approach preserves the efficiency of level set based segmentation since each connected region comprises two model parameters only. We validate the statistical basis of our model on thousands of natural images and demonstrate that our approach outperforms recent variational segment at ion methods based on second-order statistics

Power spectra of the natural input to the visual system

AbstractThe efficient coding hypothesis posits that sensory systems are adapted to the regularities of their signal input so as to reduce redundancy in the resulting representations. It is therefore important to characterize the regularities of natural signals to gain insight into the processing of natural stimuli. While measurements of statistical regularity in vision have focused on photographic images of natural environments it has been much less investigated, how the specific imaging process embodied by the organism’s eye induces statistical dependencies on the natural input to the visual system. This has allowed using the convenient assumption that natural image data are homogeneous across the visual field. Here we give up on this assumption and show how the imaging process in a human model eye influences the local statistics of the natural input to the visual system across the entire visual field. Artificial scenes with three-dimensional edge elements were generated and the influence of the imaging projection onto the back of a spherical model eye were quantified. These distributions show a strong radial influence of the imaging process on the resulting edge statistics with increasing eccentricity from the model fovea. This influence is further quantified through computation of the second order intensity statistics as a function of eccentricity from the center of projection using samples from the dead leaves image model. Using data from a naturalistic virtual environment, which allows generation of correctly projected images onto the model eye across the entire field of view, we quantified the second order dependencies as function of the position in the visual field using a new generalized parameterization of the power spectra. Finally, we compared this analysis with a commonly used natural image database, the van Hateren database, and show good agreement within the small field of view available in these photographic images. We conclude by providing a detailed quantitative analysis of the second order statistical dependencies of the natural input to the visual system across the visual field and demonstrating the importance of considering the influence of the sensory system on the statistical regularities of the input to the visual system

Optical second harmonic generation in WTe2

We study second harmonic generation (SHG) in an acentric Weyl semimetal, tungsten ditelluride (WTe2), and estimate its second-order electric susceptibility, χ(2). WTe2 is a layered material that has a natural cleavage plane perpendicular to the c-axis, but the crystal symmetry prohibits SHG emission when light is normally incident on the natural planar surface. Hence, we measure the SHG susceptibility in a laser scanning microscope, where it is easier to measure the SHG from the striated edges of the crystal. We determine the susceptibility from the variation of the SHG images with incident power, using a model that accounts for both the statistics of the SHG detection process and the surface inhomogeneities of the sample. A preliminary estimate shows that χ(2) in WTe2 is comparable to that of GaAs, a well studied nonlinear crystal with large second-order susceptibility

Image Restoration Using Joint Statistical Modeling in Space-Transform Domain

This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner. The main contributions are three-folds. First, from the perspective of image statistics, a joint statistical modeling (JSM) in an adaptive hybrid space-transform domain is established, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation. Second, a new form of minimization functional for solving image inverse problem is formulated using JSM under regularization-based framework. Finally, in order to make JSM tractable and robust, a new Split-Bregman based algorithm is developed to efficiently solve the above severely underdetermined inverse problem associated with theoretical proof of convergence. Extensive experiments on image inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions on Circuits System and Video Technology (TCSVT). High resolution pdf version and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM