Natural-Image Discrimination in the Periphery: The Importance of Phase and Amplitude Information

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 85-85
Author(s):  
G M Kennedy ◽  
D J Tolhurst
Keyword(s):  
Phase Change ◽  
Scaling Factor ◽  
Natural Images ◽  
Natural Image ◽  
Rapid Decline ◽  
Natural Scenes ◽  
Phase Identification ◽  
Phase Information ◽  
Black And White ◽  
Sinusoidal Gratings

Previous studies with simplified stimuli such as combinations of sinusoidal gratings have revealed phase identification losses in the periphery that are not eliminated by a scaling factor. How do these phase processing problems influence our ability to discriminate natural images in the periphery? In this study the ability of an observer to identify the ‘odd-image-out’ when there is either an amplitude-only, phase-only, or amplitude and phase change in one out of three stimuli is compared. Pairs of Fourier-manipulated black-and-white digitised photographs of natural images were used and phase and amplitude spectral exchanges of varying proportions were made between two different images. Measurements were made to determine the smallest phase change needed in order for the observer to reliably discriminate the manipulated image, compared to two reference stimuli, at eccentricities of 0°, 2.5°, 5°, and 10°. This was compared to discrimination thresholds found when amplitude and phase, and amplitude alone were exchanged. The ability to discriminate images on the basis of phase information alone did fall off quickly with eccentricity (comparable to phase and amplitude discriminations). However, there was a much more rapid decline in amplitude-only discrimination. It appears that phase information in natural scenes remains a relatively more important visual cue in the periphery than amplitude.

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.

Virtual evolution for visual search in natural images results in behavioral receptive fields with inhibitory surrounds

2009 ◽  
Vol 26 (1) ◽  
pp. 93-108 ◽  
Author(s):  
SHENG ZHANG ◽  
CRAIG K. ABBEY ◽  
MIGUEL P. ECKSTEIN
Keyword(s):  
Receptive Field ◽  
White Noise ◽  
Receptive Fields ◽  
Image Data ◽  
Optimization Method ◽  
Natural Images ◽  
Natural Image ◽  
Natural Scenes ◽  
Linear Combinations ◽  
Data Set

AbstractThe neural mechanisms driving perception and saccades during search use information about the target but are also based on an inhibitory surround not present in the target luminance profile (e.g., Eckstein et al., 2007). Here, we ask whether these inhibitory surrounds might reflect a strategy that the brain has adapted to optimize the search for targets in natural scenes. To test this hypothesis, we sought to estimate the best linear template (behavioral receptive field), built from linear combinations of Gabor channels representing V1 simple cells in search for an additive Gaussian target embedded in natural images. Statistically nonstationary and non-Gaussian properties of natural scenes preclude calculation of the best linear template from analytic expressions and require an iterative optimization method such as a virtual evolution via a genetic algorithm. Evolved linear receptive fields built from linear combinations of Gabor functions include substantial inhibitory surround, larger than those found in humans performing target search in white noise. The inhibitory surrounds were robust to changes in the contrast of the signal, generalized to a larger calibrated natural image data set, and tasks in which the signal occluded other objects in the image. We show that channel nonlinearities can have strong effects on the observed linear behavioral receptive field but preserve the inhibitory surrounds. Together, the results suggest that the apparent suboptimality of inhibitory surrounds in human behavioral receptive fields when searching for a target in white noise might reflect a strategy to optimize detection of signals in natural scenes. Finally, we contend that optimized linear detection of spatially compact signals in natural images might be a new possible hypothesis, distinct from decorrelation of visual input and sparse representations (e.g., Graham et al., 2006), to explain the evolution of center–surround organization of receptive fields in early vision.

scholarly journals Compressive Sensing for Natural Images

10.29007/xjzx ◽  
2018 ◽  
Author(s):  
Mayur Sevak ◽  
Anish Bagga ◽  
Arpita Agarwal ◽  
Krusha Jani
Keyword(s):  
Convex Optimization ◽  
Compressive Sensing ◽  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Haar Wavelet ◽  
Natural Images ◽  
Natural Image ◽  
Sensing Matrix ◽  
New Era ◽  
Black And White

The Compressive sensing technique is a new era of arising platform for signal processing and data acquisition. The significant statement of Compressive sensing is that recovery of certain images or signals from fewer samples than required. On the encoding side two properties of a signal is required that are incoherence and sparsity. Initially, the signal is converted into specific transform i.e. wavelet using sensing matrix it takes required coefficients that has less dimensionality than the image dimensions and thereby, we get resultant matrix which is also called as measurements which in turn are non-adaptive. Similarly, on the decoding side, due to low dimension of transmitted vector matrix, convex optimization is required to solve this problem. Convex optimization (L1 minimization) provides an answer to undetermined linear systems without the knowledge of nature of undergoing parameters through the systems.In this paper, the work is being done on natural image compression. Compression of various black and white natural images is being done with help of ‘haar wavelet’ with second level decomposition and also reconstruct the same with three measurements of 60%, 70% and 80%. Also we have measured same with PSNR (Peak Signal to Noise Ratio), RMSE (Root Mean Square Error), CoC (Correlation Coefficients) of them.

scholarly journals Robust representation of natural images by sparse and variable population of active neurons in visual cortex

2018 ◽  
Author(s):  
Takashi Yoshida ◽  
Kenichi Ohki
Keyword(s):  
Visual Cortex ◽  
Receptive Fields ◽  
Natural Images ◽  
Natural Image ◽  
Natural Scenes ◽  
Variable Population ◽  
Reliable Representation ◽  
Neuronal Patterns ◽  
Representation Scheme ◽  
Optimal Decoding

AbstractNatural scenes sparsely activate neurons in the primary visual cortex (V1). However, how sparsely active neurons robustly represent natural images and how the information is optimally decoded from the representation have not been revealed. We reconstructed natural images from V1 activity in anaesthetized and awake mice. A single natural image was linearly decodable from a surprisingly small number of highly responsive neurons, and an additional use of remaining neurons even degraded the decoding. This representation was achieved by diverse receptive fields (RFs) of the small number of highly responsive neurons. Furthermore, these neurons reliably represented the image across trials, regardless of trial-to-trial response variability. The reliable representation was supported by multiple neurons with overlapping RFs. Based on our results, the diverse, partially overlapping RFs ensure sparse and reliable representation. We propose a new representation scheme in which information is reliably represented while the representing neuronal patterns change across trials and that collecting only the activity of highly responsive neurons is an optimal decoding strategy for the downstream neurons

Coherent Compound Motion: Corners and Nonrigid Configurations

1990 ◽  
Vol 2 (1) ◽  
pp. 44-57 ◽  
Author(s):  
Steven W. Zucker ◽  
Lee Iverson ◽  
Robert A. Hummel
Keyword(s):  
Neural Computation ◽  
Coherent Motion ◽  
Natural Scenes ◽  
Motion Patterns ◽  
Motion System ◽  
Sinusoidal Gratings ◽  
Single Compound ◽  
Sharp Corners ◽  
Wire Gratings ◽  
Planar Motions

Consider two wire gratings, superimposed and moving across each other. Under certain conditions the two gratings will cohere into a single, compound pattern, which will appear to be moving in another direction. Such coherent motion patterns have been studied for sinusoidal component gratings, and give rise to percepts of rigid, planar motions. In this paper we show how to construct coherent motion displays that give rise to nonuniform, nonrigid, and nonplanar percepts. Most significantly, they also can define percepts with corners. Since these patterns are more consistent with the structure of natural scenes than rigid sinusoidal gratings, they stand as interesting stimuli for both computational and physiological studies. To illustrate, our display with sharp corners (tangent discontinuities or singularities) separating regions of coherent motion suggests that smoothing does not cross tangent discontinuities, a point that argues against existing (regularization) algorithms for computing motion. This leads us to consider how singularities can be confronted directly within optical flow computations, and we conclude with two hypotheses: (1) that singularities are represented within the motion system as multiple directions at the same retinotopic location; and (2) for component gratings to cohere, they must be at the same depth from the viewer. Both hypotheses have implications for the neural computation of coherent motion.

scholarly journals Magic Forest Illustrated by Aubrey Beardsley in “Le Morte D`Arthur”: Image Semantics and its Russian “Replication”

2021 ◽  
Vol 62 ◽  
pp. 325-343
Author(s):  
Irina V. Mischacheva ◽  
Anna P. Shlyapnikova
Keyword(s):  
Middle Ages ◽  
Forest Edge ◽  
Natural Images ◽  
Black And White ◽  
Image Semantics ◽  
Kelmscott Press ◽  
Thomas Malory ◽  
Other World

The “magic forest” illustrated by Aubrey Beardsley in spite of the continuity in relation to the Pre-Raphaelite and the reconstructed Middle Ages / Renaissance in the works, dedicated to Arthur on the pages of the Kelmscott Press publications, has a number of peculiar features. The semantics of the natural images of the black-and-white illustrations to Thomas Malory's “Le Morte D`Arthur” turns out to be consonant with both the folklore (pagan in its essence) ideas about the forest as other world, and the Christian symbolism of the passion forest, this uncultivated “exile lands”. The essential features of the “Beardsley`s forest” can include its gloominess (black grass, spectacular haze of frames), inaccessibility (thickets of giant bindweed “stifling” knights, fence of trunks, represented as the border of the forest edge, thorns, reminding of the torments of earthly love and its sinfulness). Thomas Malory reduces the element of unbelievable in his narration; Beardsley, on the contrary, returns dragons, fairies, satyrs to the Forest. The paper addresses the background of the first publications of his “forest” graphics in Russia, notes the transfer of emphasis from the medieval forest topic to the motif of the landscape garden that is more consonant with the rockail aesthetics. The authors also draw comparison of interpretation of the forest image and its goat-footed guardians, satyrs, in the representation of the English illustrator and in the text of the “Northern Symphony” by A. Bely.

Binary Mixture of Solid-Solid Phase Change Material: Synthesis, Characterization and Experimental Study

2020 ◽  
Vol 978 ◽  
pp. 407-420
Author(s):  
Cyril Reuben Raj ◽  
S. Suresh ◽  
Arijit Upadhyay ◽  
K. Akash Govind ◽  
R. Nivethaa
Keyword(s):  
Phase Change ◽  
Phase Change Material ◽  
Solid Phase ◽  
Electronic System ◽  
Laser Flash ◽  
Phase Identification ◽  
Heating And Cooling ◽  
Blending Method ◽  
Phase Transition Temperatures ◽  
Change Material

In this work, a class of polyol solid-solid phase change material where Neopentyl glycol is mixed in 6 and 2 wt.% of Pentaerythritol and was synthesized by physical blending method to obtain homogeneous mixture and thermally cycled for 500 times. The surface morphology, chemical composition, crystal phase identification, thermal degradation, and phase change phenomena were characterized. The phase transition temperatures and enthalpies upon heating and cooling of 6 and 2 wt.% of Pentaerythritol are found to be 43.1 °C, 133 J g-1, and 28.2 °C, 119 J g-1, and 41.2 °C, 121 J g-1, and 28.5 °C, 106 J g-1, respectively which suits for electronic system to keep under operating zone. Laser Flash Apparatus was used to find the thermal diffusivity and thermal conductivity value was calculated. Further, the effect of heat transfer in binary polyol mixtures were experimentally analysed through conventional heat sink for electronic cooling application.

scholarly journals Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis

2019 ◽  
Vol 12 (1) ◽  
pp. 86 ◽  
Author(s):  
Rafael Pires de Lima ◽  
Kurt Marfurt
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Network Models ◽  
Remote Sensing Image ◽  
Natural Images ◽  
Natural Image ◽  
Scene Classification ◽  
Neural Network Models

Remote-sensing image scene classification can provide significant value, ranging from forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial photographs of the early 20th century to the satellite imagery of today, the amount of remote-sensing data has increased geometrically with a higher resolution. The need to analyze these modern digital data motivated research to accelerate remote-sensing image classification. Fortunately, great advances have been made by the computer vision community to classify natural images or photographs taken with an ordinary camera. Natural image datasets can range up to millions of samples and are, therefore, amenable to deep-learning techniques. Many fields of science, remote sensing included, were able to exploit the success of natural image classification by convolutional neural network models using a technique commonly called transfer learning. We provide a systematic review of transfer learning application for scene classification using different datasets and different deep-learning models. We evaluate how the specialization of convolutional neural network models affects the transfer learning process by splitting original models in different points. As expected, we find the choice of hyperparameters used to train the model has a significant influence on the final performance of the models. Curiously, we find transfer learning from models trained on larger, more generic natural images datasets outperformed transfer learning from models trained directly on smaller remotely sensed datasets. Nonetheless, results show that transfer learning provides a powerful tool for remote-sensing scene classification.

scholarly journals Informative features of local field potential signals in primary visual cortex during natural image stimulation

2015 ◽  
Vol 113 (5) ◽  
pp. 1520-1532 ◽  
Author(s):  
Mojtaba Seyedhosseini ◽  
S. Shushruth ◽  
Tyler Davis ◽  
Jennifer M. Ichida ◽  
Paul A. House ◽  
...  
Keyword(s):  
Local Field ◽  
Local Field Potential ◽  
Field Potential ◽  
Synaptic Activity ◽  
Natural Images ◽  
Gabor Wavelet ◽  
Network Activity ◽  
Natural Image ◽  
Specific Information ◽  
Image Identification

The local field potential (LFP) is of growing importance in neurophysiology as a metric of network activity and as a readout signal for use in brain-machine interfaces. However, there are uncertainties regarding the kind and visual field extent of information carried by LFP signals, as well as the specific features of the LFP signal conveying such information, especially under naturalistic conditions. To address these questions, we recorded LFP responses to natural images in V1 of awake and anesthetized macaques using Utah multielectrode arrays. First, we have shown that it is possible to identify presented natural images from the LFP responses they evoke using trained Gabor wavelet (GW) models. Because GW models were devised to explain the spiking responses of V1 cells, this finding suggests that local spiking activity and LFPs (thought to reflect primarily local synaptic activity) carry similar visual information. Second, models trained on scalar metrics, such as the evoked LFP response range, provide robust image identification, supporting the informative nature of even simple LFP features. Third, image identification is robust only for the first 300 ms following image presentation, and image information is not restricted to any of the spectral bands. This suggests that the short-latency broadband LFP response carries most information during natural scene viewing. Finally, best image identification was achieved by GW models incorporating information at the scale of ∼0.5° in size and trained using four different orientations. This suggests that during natural image viewing, LFPs carry stimulus-specific information at spatial scales corresponding to few orientation columns in macaque V1.

scholarly journals Pareidolia in Schizophrenia and Bipolar Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Eid G. Abo Hamza ◽  
Szabolcs Kéri ◽  
Katalin Csigó ◽  
Dalia Bedewy ◽  
Ahmed A. Moustafa
Keyword(s):  
Bipolar Disorder ◽  
Natural Scenes ◽  
Healthy Controls ◽  
Black And White ◽  
The Face ◽  
Noise Test ◽  
Bipolar Patients ◽  
Blurred Images ◽  
Human Faces ◽  
First Time

While there are many studies on pareidolia in healthy individuals and patients with schizophrenia, to our knowledge, there are no prior studies on pareidolia in patients with bipolar disorder. Accordingly, in this study, we, for the first time, measured pareidolia in patients with bipolar disorder (N = 50), and compared that to patients with schizophrenia (N = 50) and healthy controls (N = 50). We have used (a) the scene test, which consists of 10 blurred images of natural scenes that was previously found to produce illusory face responses and (b) the noise test which had 32 black and white images consisting of visual noise and 8 images depicting human faces; participants indicated whether a face was present on these images and to point to the location where they saw the face. Illusory responses were defined as answers when observers falsely identified objects that were not on the images in the scene task (maximum illusory score: 10), and the number of noise images in which they reported the presence of a face (maximum illusory score: 32). Further, we also calculated the total pareidolia score for each task (the sum number of images with illusory responses in the scene and noise tests). The responses were scored by two independent raters with an excellent congruence (kappa > 0.9). Our results show that schizophrenia patients scored higher on pareidolia measures than both healthy controls and patients with bipolar disorder. Our findings are agreement with prior findings on more impaired cognitive processes in schizophrenia than in bipolar patients.

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