scholarly journals Mice use robust and common strategies to discriminate natural scenes

2017 ◽  
Author(s):  
Yiyi Yu ◽  
Riichiro Hira ◽  
Jeffrey N. Stirman ◽  
Waylin Yu ◽  
Ikuko T. Smith ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Discrimination Performance ◽  
Structural Similarity ◽  
Orientation Dependence ◽  
Natural Scenes ◽  
Natural Environments ◽  
Natural Scene ◽  
Image Metrics ◽  
Screen System ◽  
Mouse Vision

AbstractMice use vision to navigate and avoid predators in natural environments. However, the spatial resolution of mouse vision is poor compared to primates, and mice lack a fovea. Thus, it is unclear how well mice can discriminate ethologically relevant scenes. Here, we examined natural scene discrimination in mice using an automated touch-screen system. We estimated the discrimination difficulty using the computational metric structural similarity (SSIM), and constructed psychometric curves. However, the performance of each mouse was better predicted by the population mean than SSIM. This high inter-mouse agreement indicates that mice use common and robust strategies to discriminate natural scenes. We tested several other image metrics to find an alternative to SSIM for predicting discrimination performance. We found that a simple, primary visual cortex (V1)-inspired model predicted mouse performance with fidelity approaching the inter-mouse agreement. The model involved convolving the images with Gabor filters, and its performance varied with the orientation of the Gabor filter. This orientation dependence was driven by the stimuli, rather than an innate biological feature. Together, these results indicate that mice are adept at discriminating natural scenes, and their performance is well predicted by simple models of V1 processing.

scholarly journals Cortical Sensitivity to Natural Scene Structure

2019 ◽  
Author(s):  
Daniel Kaiser ◽  
Greta Häberle ◽  
Radoslaw M. Cichy
Keyword(s):  
Spatial Structure ◽  
Human Vision ◽  
Scene Analysis ◽  
Natural Scenes ◽  
Natural Environments ◽  
Cortical Processing ◽  
Natural Scene ◽  
Visual Inputs ◽  
Scene Structure ◽  
Categorical Structure

AbstractNatural scenes are inherently structured, with meaningful objects appearing in predictable locations. Human vision is tuned to this structure: When scene structure is purposefully jumbled, perception is strongly impaired. Here, we tested how such perceptual effects are reflected in neural sensitivity to scene structure. During separate fMRI and EEG experiments, participants passively viewed scenes whose spatial structure (i.e., the position of scene parts) and categorical structure (i.e., the content of scene parts) could be intact or jumbled. Using multivariate decoding, we show that spatial (but not categorical) scene structure profoundly impacts on cortical processing: Scene-selective responses in occipital and parahippocampal cortices (fMRI) and after 255ms (EEG) accurately differentiated between spatially intact and jumbled scenes. Importantly, this differentiation was more pronounced for upright than for inverted scenes, indicating genuine sensitivity to spatial structure rather than sensitivity to low-level attributes. Our findings suggest that visual scene analysis is tightly linked to the spatial structure of our natural environments. This link between cortical processing and scene structure may be crucial for rapidly parsing naturalistic visual inputs.

scholarly journals Spatial vision by macaque midbrain

2017 ◽  
Author(s):  
Chih-Yang Chen ◽  
Lukas Sonnenberg ◽  
Simone Weller ◽  
Thede Witschel ◽  
Ziad M. Hafed
Keyword(s):  
Superior Colliculus ◽  
Spatial Vision ◽  
Population Level ◽  
Visual Exploration ◽  
Natural Scenes ◽  
Natural Environments ◽  
Natural Scene ◽  
Natural Scene Statistics ◽  
Brain Areas ◽  
Spatial Frequencies

Visual brain areas exhibit tuning characteristics that are well suited for image statistics present in our natural environment. However, visual sensation is an active process, and if there are any brain areas that ought to be particularly 'in tune' with natural scene statistics, it would be sensory-motor areas critical for guiding behavior. Here we found that the primate superior colliculus, a structure instrumental for rapid visual exploration with saccades, detects low spatial frequencies, which are the most prevalent in natural scenes, much more rapidly than high spatial frequencies. Importantly, this accelerated detection happens independently of whether a neuron is more or less sensitive to low spatial frequencies to begin with. At the population level, the superior colliculus additionally over-represents low spatial frequencies in neural response sensitivity, even at near-foveal eccentricities. Thus, the superior colliculus possesses both temporal and response gain mechanisms for efficient gaze realignment in low-spatial-frequency dominated natural environments.

scholarly journals Generalist camouflage can be more successful than microhabitat specialisation in natural environments

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Emmanuelle Sophie Briolat ◽  
Lina María Arenas ◽  
Anna E. Hughes ◽  
Eric Liggins ◽  
Martin Stevens
Keyword(s):  
Survival Benefit ◽  
Search Task ◽  
Natural Scenes ◽  
Science Methods ◽  
Natural Environments ◽  
Background Matching ◽  
Computer Based ◽  
Visual Scenes ◽  
Small Targets ◽  
Predator Defence

Abstract Background Crypsis by background-matching is a critical form of anti-predator defence for animals exposed to visual predators, but achieving effective camouflage in patchy and variable natural environments is not straightforward. To cope with heterogeneous backgrounds, animals could either specialise on particular microhabitat patches, appearing cryptic in some areas but mismatching others, or adopt a compromise strategy, providing partial matching across different patch types. Existing studies have tested the effectiveness of compromise strategies in only a limited set of circumstances, primarily with small targets varying in pattern, and usually in screen-based tasks. Here, we measured the detection risk associated with different background-matching strategies for relatively large targets, with human observers searching for them in natural scenes, and focusing on colour. Model prey were designed to either ‘specialise’ on the colour of common microhabitat patches, or ‘generalise’ by matching the average colour of the whole visual scenes. Results In both the field and an equivalent online computer-based search task, targets adopting the generalist strategy were more successful in evading detection than those matching microhabitat patches. This advantage occurred because, across all possible locations in these experiments, targets were typically viewed against a patchwork of different microhabitat areas; the putatively generalist targets were thus more similar on average to their various immediate surroundings than were the specialists. Conclusions Demonstrating close agreement between the results of field and online search experiments provides useful validation of online citizen science methods commonly used to test principles of camouflage, at least for human observers. In finding a survival benefit to matching the average colour of the visual scenes in our chosen environment, our results highlight the importance of relative scales in determining optimal camouflage strategies, and suggest how compromise coloration can succeed in nature.

Independence of color and luminance edges in natural scenes

2009 ◽  
Vol 26 (1) ◽  
pp. 35-49 ◽  
Author(s):  
THORSTEN HANSEN ◽  
KARL R. GEGENFURTNER
Keyword(s):  
Visual Processing ◽  
Artificial Vision ◽  
Natural Scenes ◽  
Natural Scene ◽  
Natural Scene Statistics ◽  
Form Vision ◽  
Independent Information ◽  
Edge Contrast ◽  
Source Of Information ◽  
Gain Access

AbstractForm vision is traditionally regarded as processing primarily achromatic information. Previous investigations into the statistics of color and luminance in natural scenes have claimed that luminance and chromatic edges are not independent of each other and that any chromatic edge most likely occurs together with a luminance edge of similar strength. Here we computed the joint statistics of luminance and chromatic edges in over 700 calibrated color images from natural scenes. We found that isoluminant edges exist in natural scenes and were not rarer than pure luminance edges. Most edges combined luminance and chromatic information but to varying degrees such that luminance and chromatic edges were statistically independent of each other. Independence increased along successive stages of visual processing from cones via postreceptoral color-opponent channels to edges. The results show that chromatic edge contrast is an independent source of information that can be linearly combined with other cues for the proper segmentation of objects in natural and artificial vision systems. Color vision may have evolved in response to the natural scene statistics to gain access to this independent information.

Face Detection in Natural Scenes

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 67-67 ◽  
Author(s):  
H Hill ◽  
R Watt
Keyword(s):  
Face Detection ◽  
Face Processing ◽  
Feature Detection ◽  
Processing System ◽  
Decision Task ◽  
Natural Scenes ◽  
Natural Scene ◽  
Energy Models ◽  
The Face ◽  
And Performance

The first task of any face processing system is detection of the face. We studied how the human visual system achieves face detection using a 2AFC task in which subjects were required to detect a face in the image of a natural scene. Luminance noise was added to the stimuli and performance was measured as a function of orientation and orientation bandwidth of the noise. Sensitivity levels and the effects of orientation bandwidth were similar for horizontally and vertically oriented noise. Performance was reduced for the smallest orientation bandwidth (5.6°) noise but sensitivity did not decrease further with increasing bandwidth until a point between 45° and 90°. The results suggest that important information may be oriented close to the vertical and horizontal. To test whether the results were specific to the task of face detection the same noise was added to the images in a man-made natural decision task. Equivalent levels of noise were found to be more disruptive and the effect of orientation bandwidth was different. The results are discussed in terms of models of face processing making use of oriented filters (eg Watt and Dakin, 1993 Perception22 Supplement, 12) and local energy models of feature detection (Morrone and Burr, 1988 Proceedings of the Royal Society of London B235 221 – 245).

scholarly journals Maxwellian Eye Fixation during Natural Scene Perception

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Jean Duchesne ◽  
Vincent Bouvier ◽  
Julien Guillemé ◽  
Olivier A. Coubard
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Scene Perception ◽  
Random Motion ◽  
Natural Scenes ◽  
Movement Amplitude ◽  
Natural Scene ◽  
Eye Fixation ◽  
Free Viewing ◽  
Natural Scene Perception

When we explore a visual scene, our eyes make saccades to jump rapidly from one area to another and fixate regions of interest to extract useful information. While the role of fixation eye movements in vision has been widely studied, their random nature has been a hitherto neglected issue. Here we conducted two experiments to examine the Maxwellian nature of eye movements during fixation. In Experiment 1, eight participants were asked to perform free viewing of natural scenes displayed on a computer screen while their eye movements were recorded. For each participant, the probability density function (PDF) of eye movement amplitude during fixation obeyed the law established by Maxwell for describing molecule velocity in gas. Only the mean amplitude of eye movements varied with expertise, which was lower in experts than novice participants. In Experiment 2, two participants underwent fixed time, free viewing of natural scenes and of their scrambled version while their eye movements were recorded. Again, the PDF of eye movement amplitude during fixation obeyed Maxwell’s law for each participant and for each scene condition (normal or scrambled). The results suggest that eye fixation during natural scene perception describes a random motion regardless of top-down or of bottom-up processes.

Image Structure at the Center of Gaze during Free Viewing

2006 ◽  
Vol 18 (5) ◽  
pp. 737-748 ◽  
Author(s):  
Valentin Dragoi ◽  
Mriganka Sur
Keyword(s):  
Eye Movements ◽  
Rhesus Monkeys ◽  
Discrimination Performance ◽  
Natural Scenes ◽  
Image Patch ◽  
Image Scanning ◽  
Retinal Stimulation ◽  
Free Viewing ◽  
Precise Knowledge ◽  
Orientation Structure

It is generally believed that the visual system is adapted to the statistics of the visual world. Measuring and understanding these statistics require precise knowledge of the structure of the signals reaching fovea during image scanning. However, despite the fact that eye movements cause retinal stimulation to change several times in a second, it is implicitly assumed that images are sampled uniformly during natural viewing. By analyzing the eye movements of three rhesus monkeys freely viewing natural scenes, we report here significant anisotropy in stimulus statistics at the center of gaze. We find that fixation on an image patch is more likely to be followed by a saccade to a nearby patch of similar orientation structure or by a saccade to a more distant patch of largely dissimilar orientation structure. Furthermore, we show that orientation-selective neurons in the primary visual cortex (V1) can take advantage of eye movement statistics to selectively improve their discrimination performance.

scholarly journals Effect of Geometric Sharpness on Translucent Material Perception

2019 ◽  
Author(s):  
Bei Xiao ◽  
Shuang Zhao ◽  
Ioannis Gkioulekas ◽  
Wenyan Bi ◽  
Kavita Bala
Keyword(s):  
Optical Properties ◽  
Similarity Index ◽  
Human Perception ◽  
Structural Similarity ◽  
Target Object ◽  
Light Transport ◽  
Optical Parameters ◽  
Image Metrics ◽  
3D Geometry ◽  
Object Shapes

When judging optical properties of a translucent object, humans often look at sharp geometric features such as edges and thin parts. Analysis of the physics of light transport shows that these sharp geometries are necessary for scientific imaging systems to be able to accurately measure the underlying material optical properties. In this paper, we examine whether human perception of translucency is likewise affected by the presence of sharp geometry, by confounding our perceptual inferences about an object’s optical properties. We employ physically accurate simulations to create visual stimuli of translucent materials with varying shapes and optical properties under different illuminations. We then use these stimuli in psychophysical experiments, where human observers are asked to match an image of a target object by adjusting the material parameters of a match object with different geometric sharpness, lighting geometry, and 3D geometry. We find that the level of geometric sharpness significantly affects perceived translucency by the observers. These findings generalize across a few illuminations and object shapes. Our results suggest that the perceived translucency of an object depends on both the underlying material optical parameters and 3D shape. We also conduct analyses using computational metrics including (luminance-normalized) L2, structural similarity index (SSIM), and Michelson contrast. We find that these image metrics cannot predict perceptual results, suggesting low level image cues are not sufficient to explain our results.

scholarly journals Efficient coding of natural scenes improves neural system identification

2022 ◽  
Author(s):  
Yongrong Qiu ◽  
David A Klindt ◽  
Klaudia P Szatko ◽  
Dominic Gonschorek ◽  
Larissa Hoefling ◽  
...  
Keyword(s):  
System Identification ◽  
Visual Processing ◽  
Neural System ◽  
Natural Scenes ◽  
Natural Environments ◽  
Retinal Neurons ◽  
Efficient Coding ◽  
Recorded Data ◽  
Identification Model ◽  
Regularized Model

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage coding principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the stand-alone system identification model, it also produced more biologically-plausible filters. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing.

scholarly journals Structural Similarity Measurement Based Cost Function for Stereo Matching of Automotive Applications

2020 ◽  
Vol 6 (8) ◽  
pp. 77
Author(s):  
Oussama Zeglazi ◽  
Mohammed Rziza ◽  
Aouatif Amine ◽  
Cédric Demonceaux
Keyword(s):  
Stereo Matching ◽  
Road Traffic ◽  
Structural Information ◽  
Structural Similarity ◽  
Natural Scenes ◽  
Human Visual Perception ◽  
Adaptive Aggregation ◽  
Aggregation Technique ◽  
Image Gradients ◽  
Two Stages

The human visual perception uses structural information to recognize stereo correspondences in natural scenes. Therefore, structural information is important to build an efficient stereo matching algorithm. In this paper, we demonstrate that incorporating the structural information similarity, extracted either from image intensity (SSIM) directly or from image gradients (GSSIM), between two patches can accurately describe the patch structures and, thus, provides more reliable initial cost values. We also address one of the major phenomenons faced in stereo matching for real world scenes, radiometric changes. The performance of the proposed cost functions was evaluated within two stages: the first one considers these costs without aggregation process while the second stage uses the fast adaptive aggregation technique. The experiments were conducted on the real road traffic scenes KITTI 2012 and KITTI 2015 benchmarks. The obtained results demonstrate the potential merits of the proposed stereo similarity measurements under radiometric changes.

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