The Use of Visual Statistical Features in Empirical Aesthetics

2020 ◽  
Author(s):  
Daniel Graham
Keyword(s):  
Visual System ◽  
Natural Environment ◽  
Natural Images ◽  
Natural Scenes ◽  
Statistical Features ◽  
Statistical Structure ◽  
Future Directions ◽  
Visual Aesthetics ◽  
Processing Strategies ◽  
The Brain

Evolution generally demands that the brain take advantage of the probable statistical structure in the natural environment. Much research in recent decades has confirmed that regular statistical features in natural scenes—especially low-level spatial regularities—can help explain processing strategies in the human visual system. Basic statistical features in various classes of human-created images broadly match those found in natural scenes. Such regularities can be seen as evolved constraints on the visual structure of aesthetic images and therefore human visual aesthetics. Some researchers have also attempted to find statistical features whose variation from natural images is associated with variations in preference and other aesthetic variables. There is evidence that variations in statistical features of luminance and color could be exploited by the visual system in certain situations. However, there is much ambiguity and variability in most reported relationships between variations in image statistical features and variations in measures of human aesthetics. In contrast, basic statistical constraints that align with efficient visual system processing are almost never violated in aesthetic images. Put simply, statistical features may constrain but may not explain variability in visual aesthetics. The chapter concludes with an outlook on future directions for research.

scholarly journals Systematic differences between visually-relevant global and local image statistics of brain MRI and natural scenes

2018 ◽  
Author(s):  
Yueyang Xu ◽  
Ashish Raj ◽  
Jonathan Victor ◽  
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Image Formation ◽  
Natural Images ◽  
Natural Image ◽  
Natural Scenes ◽  
Image Statistics ◽  
Processing Technologies ◽  
Global And Local ◽  
Local Image

AbstractAn important heuristic in developing image processing technologies is to mimic the computational strategies used by humans. Relevant to this, recent studies have shown that the human brain’s processing strategy is closely matched to the characteristics of natural scenes, both in terms of global and local image statistics. However, structural MRI images and natural scenes have fundamental differences: the former are two-dimensional sections through a volume, the latter are projections. MRI image formation is also radically different from natural image formation, involving acquisition in Fourier space, followed by several filtering and processing steps that all have the potential to alter image statistics. As a consequence, aspects of the human visual system that are finely-tuned to processing natural scenes may not be equally well-suited for MRI images, and identification of the differences between MRI images and natural scenes may lead to improved machine analysis of MRI.With these considerations in mind, we analyzed spectra and local image statistics of MRI images in several databases including T1 and FLAIR sequence types and of simulated MRI images,[1]–[6] and compared this analysis to a parallel analysis of natural images[7] and visual sensitivity[7][8]. We found substantial differences between the statistical features of MRI images and natural images. Power spectra of MRI images had a steeper slope than that of natural images, indicating a lack of scale invariance. Independent of this, local image statistics of MRI and natural images differed: compared to natural images, MRI images had smaller variations in their local two-point statistics and larger variations in their local three-point statistics – to which the human visual system is relatively insensitive. Our findings were consistent across MRI databases and simulated MRI images, suggesting that they result from brain geometry at the scale of MRI resolution, rather than characteristics of specific imaging and reconstruction methods.

scholarly journals Spatiochromatic Receptive Field Properties Derived from Information-Theoretic Analyses of Cone Mosaic Responses to Natural Scenes

2003 ◽  
Vol 15 (2) ◽  
pp. 397-417 ◽  
Author(s):  
Eizaburo Doi ◽  
Toshio Inui ◽  
Te-Won Lee ◽  
Thomas Wachtler ◽  
Terrence J. Sejnowski
Keyword(s):  
Visual System ◽  
Receptive Fields ◽  
Independent Component ◽  
Natural Images ◽  
Natural Scenes ◽  
Subsequent Stage ◽  
Type I ◽  
Sensory Signals ◽  
Cone Mosaic ◽  
Unsupervised Neural Network

Neurons in the early stages of processing in the primate visual system efficiently encode natural scenes. In previous studies of the chromatic properties of natural images, the inputs were sampled on a regular array, with complete color information at every location. However, in the retina cone photoreceptors with different spectral sensitivities are arranged in a mosaic. We used an unsupervised neural network model to analyze the statistical structure of retinal cone mosaic responses to calibrated color natural images. The second-order statistical dependencies derived from the covariance matrix of the sensory signals were removed in the first stage of processing. These decorrelating filters were similar to type I receptive fields in parvo- or konio-cellular LGN in both spatial and chromatic characteristics. In the subsequent stage, the decorrelated signals were linearly transformed to make the output as statistically independent as possible, using independent component analysis. The independent component filters showed luminance selectivity with simple-cell-like receptive fields, or had strong color selectivity with large, often double-opponent, receptive fields, both of which were found in the primary visual cortex (V1). These results show that the “form” and “color” channels of the early visual system can be derived from the statistics of sensory signals.

S-cone discrimination for stimuli with spatial and temporal chromatic contrast

2008 ◽  
Vol 25 (3) ◽  
pp. 349-354 ◽  
Author(s):  
DINGCAI CAO ◽  
ANDREW J. ZELE ◽  
VIVIANNE C. SMITH ◽  
JOEL POKORNY
Keyword(s):  
Visual System ◽  
Natural Environment ◽  
Stimulus Condition ◽  
Spatial And Temporal Variation ◽  
Natural Scenes ◽  
Test Field ◽  
Temporal Contrast ◽  
Chromatic Contrast ◽  
Laboratory Methods ◽  
Spatio Temporal

In the natural environment, color discriminations are made within a rich context of spatial and temporal variation. In classical laboratory methods for studying chromatic discrimination, there is typically a border between the test and adapting fields that introduces a spatial chromatic contrast signal. Typically, the roles of spatial and temporal contrast on chromatic discrimination are not assessed in the laboratory approach. In this study, S-cone discrimination was measured using stimulus paradigms that controlled the level of spatio-temporal S-cone contrast between the tests and adapting fields. The results indicate that S-cone discrimination of chromaticity differences between a pedestal and adapting surround is equivalent for stimuli containing spatial, temporal or spatial-and-temporal chromatic contrast between the test field and the surround. For a stimulus condition that did not contain spatial or temporal contrast, the visual system adapted to the pedestal instead of the surround. The data are interpreted in terms of a model consistent with primate koniocellular pathway physiology. The paradigms provide an approach for studying the effects of spatial and temporal contrast on discrimination in natural scenes.

scholarly journals Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure

2021 ◽  
Author(s):  
Been Kim ◽  
Emily Reif ◽  
Martin Wattenberg ◽  
Samy Bengio ◽  
Michael C. Mozer
Keyword(s):  
State Of The Art ◽  
Ecological Validity ◽  
Past Research ◽  
Natural Images ◽  
Natural Scenes ◽  
Human Visual Perception ◽  
Statistical Structure ◽  
Visual Elements ◽  
Complete Figure ◽  
Gestalt Laws

AbstractThe Gestalt laws of perceptual organization, which describe how visual elements in an image are grouped and interpreted, have traditionally been thought of as innate. Given past research showing that these laws have ecological validity, we investigate whether deep learning methods infer Gestalt laws from the statistics of natural scenes. We examine the law of closure, which asserts that human visual perception tends to “close the gap” by assembling elements that can jointly be interpreted as a complete figure or object. We demonstrate that a state-of-the-art convolutional neural network, trained to classify natural images, exhibits closure on synthetic displays of edge fragments, as assessed by similarity of internal representations. This finding provides further support for the hypothesis that the human perceptual system is even more elegant than the Gestaltists imagined: a single law—adaptation to the statistical structure of the environment—might suffice as fundamental.

scholarly journals Spatial whitening in the retina may be necessary for V1 to learn a sparse representation of natural scenes

2019 ◽  
Author(s):  
Eric McVoy Dodds ◽  
Jesse Alexander Livezey ◽  
Michael Robert DeWeese
Keyword(s):  
Visual System ◽  
Sparse Coding ◽  
Natural Images ◽  
Natural Scenes ◽  
Local Learning ◽  
High Fidelity ◽  
Retinal Ganglion ◽  
Learning Rules ◽  
Highly Correlated ◽  
Sparse Features

AbstractRetinal ganglion cell outputs are less correlated across space than are natural scenes, and it has been suggested that this decorrelation is performed in the retina in order to improve efficiency and to benefit processing later in the visual system. However, sparse coding, a successful computational model of primary visual cortex, is achievable under some conditions with highly correlated inputs: most sparse coding algorithms learn the well-known sparse features of natural images and can output sparse, high-fidelity codes with or without a preceding decorrelation stage of processing. We propose that sparse coding with biologically plausible local learning rules does require decorrelated inputs, providing a possible explanation for why whitening may be necessary early in the visual system.

scholarly journals Conscious perception of natural images is constrained by category-related visual features

2019 ◽  
Author(s):  
Daniel Lindh ◽  
Ilja G. Sligte ◽  
Sara Assecondi ◽  
Kimron L. Shapiro ◽  
Ian Charest
Keyword(s):  
Visual System ◽  
Natural Images ◽  
Visual Features ◽  
Adaptive Behaviour ◽  
Conscious Perception ◽  
Activation Patterns ◽  
Visual Objects ◽  
High Level ◽  
The Brain ◽  
Insight Into

AbstractConscious perception is crucial for adaptive behaviour yet access to consciousness varies for different types of objects. The visual system comprises regions with widely distributed category information and exemplar-level representations that cluster according to category. Does this categorical organisation in the brain provide insight into object-specific access to consciousness? We address this question using the Attentional Blink (AB) approach with visual objects as targets. We find large differences across categories in the AB then employ activation patterns extracted from a deep convolutional neural network (DCNN) to reveal that these differences depend on mid- to high-level, rather than low-level, visual features. We further show that these visual features can be used to explain variance in performance across trials. Taken together, our results suggest that the specific organisation of the higher-tier visual system underlies important functions relevant for conscious perception of differing natural images.

scholarly journals Conscious perception of natural images is constrained by category-related visual features

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Daniel Lindh ◽  
Ilja G. Sligte ◽  
Sara Assecondi ◽  
Kimron L. Shapiro ◽  
Ian Charest
Keyword(s):  
Visual System ◽  
Attentional Blink ◽  
Natural Images ◽  
Visual Features ◽  
Adaptive Behaviour ◽  
Conscious Perception ◽  
Activation Patterns ◽  
Visual Objects ◽  
High Level ◽  
The Brain

Abstract Conscious perception is crucial for adaptive behaviour yet access to consciousness varies for different types of objects. The visual system comprises regions with widely distributed category information and exemplar-level representations that cluster according to category. Does this categorical organisation in the brain provide insight into object-specific access to consciousness? We address this question using the Attentional Blink approach with visual objects as targets. We find large differences across categories in the attentional blink. We then employ activation patterns extracted from a deep convolutional neural network to reveal that these differences depend on mid- to high-level, rather than low-level, visual features. We further show that these visual features can be used to explain variance in performance across trials. Taken together, our results suggest that the specific organisation of the higher-tier visual system underlies important functions relevant for conscious perception of differing natural images.

Author response for "The brain of the African wild dog. IV . The visual system"

2020 ◽  
Author(s):  
Samson Chengetanai ◽  
Adhil Bhagwandin ◽  
Mads F. Bertelsen ◽  
Therese Hård ◽  
Patrick R. Hof ◽  
...  
Keyword(s):  
Visual System ◽  
Author Response ◽  
African Wild Dog ◽  
Wild Dog ◽  
The Brain

Neuro-Optometric Treatment of Complications from a Cerebellar Astrocytoma

2020 ◽  
pp. 304-312
Keyword(s):  
Visual System ◽  
Common Site ◽  
Us Army ◽  
Cranial Nerve Palsies ◽  
Pilocytic Astrocytomas ◽  
The Us ◽  
Extent Of Damage ◽  
Functional Components ◽  
The Brain

Background: Insult to the brain, whether from trauma or other etiologies, can have a devastating effect on an individual. Symptoms can be many and varied, depending on the location and extent of damage. This presentation can be a challenge to the optometrist charged with treating the sequelae of this event as multiple functional components of the visual system can be affected. Case Report: This paper describes the diagnosis and subsequent ophthalmic management of an acquired brain injury in a 22 year old male on active duty in the US Army. After developing acute neurological symptoms, the patient was diagnosed with a pilocytic astrocytoma of the cerebellum. Emergent neurosurgery to treat the neoplasm resulted in iatrogenic cranial nerve palsies and a hemispheric syndrome. Over the next 18 months, he was managed by a series of providers, including a strabismus surgeon, until presenting to our clinic. Lenses, prism, and in-office and out-of-office neurooptometric rehabilitation therapy were utilized to improve his functioning and make progress towards his goals. Conclusions: Pilocytic astrocytomas are the most common primary brain tumors, and the vast majority are benign with excellent surgical prognosis. Although the most common site is the cerebellum, the visual pathway is also frequently affected. If the eye or visual system is affected, optometrists have the ability to drastically improve quality of life with neuro-optometric rehabilitation.

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