Brightness Filling-in of Natural Images

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 136-136
Author(s):  
J H Elder
Keyword(s):  
Computational Model ◽  
Surface Brightness ◽  
Temporal Dynamics ◽  
Scale Space ◽  
Natural Images ◽  
Original Image ◽  
Edge Point ◽  
Contour Representation ◽  
Psychophysical Studies ◽  
Space Method

There is both psychophysical and physiological evidence that the perception of brightness variations in an image may be the result of a filling-in process in which the luminance signal is encoded only at image contours and is then neurally diffused to form representations of surface brightness. Despite this evidence, the filling-in hypothesis remains controversial. One problem is that in previous experiments highly simplified synthetic stimuli have been used; it is unclear whether brightness filling-in is feasible for complex natural images containing shading, shadows, and focal blur. To address this question, we present a computational model for brightness filling-in and results of experiments which test the model on a large and diverse set of natural images. The model is based on a scale-space method for edge detection which computes a contour code consisting of estimates of position, brightness, contrast, and blur at each edge point in an image (Elder and Zucker, 1996, paper presented at ECCV). This representation is then inverted by a diffusion-based filling-in algorithm which reconstructs an estimate of the original image. Psychophysical assessment of results shows that while filling-in of brightness alone leads to significant artifact, parallel filling-in of both brightness and blur produces perceptually accurate reconstructions. The temporal dynamics of blur reconstruction predicted by the model are consistent with psychophysical studies of blur perception (Westheimer, 1991 Journal of the Optical Society of America A8 681 – 685). These results suggest that a scale-adaptive contour representation can in principle capture the information needed for the perceptually accurate filling-in of complex natural images.

scholarly journals Interacting roles of lateral and medial Orbitofrontal cortex in decision-making and learning : A system-level computational model

2019 ◽  
Author(s):  
Bhargav Teja Nallapu ◽  
Frédéric Alexandre
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Computational Model ◽  
Orbitofrontal Cortex ◽  
Temporal Dynamics ◽  
System Level ◽  
Interacting Networks ◽  
Cortical Regions ◽  
Experimental Findings

AbstractIn the context of flexible and adaptive animal behavior, the orbitofrontal cortex (OFC) is found to be one of the crucial regions in the prefrontal cortex (PFC) influencing the downstream processes of decision-making and learning in the sub-cortical regions. Although OFC has been implicated to be important in a variety of related behavioral processes, the exact mechanisms are unclear, through which the OFC encodes or processes information related to decision-making and learning. Here, we propose a systems-level view of the OFC, positioning it at the nexus of sub-cortical systems and other prefrontal regions. Particularly we focus on one of the most recent implications of neuroscientific evidences regarding the OFC - possible functional dissociation between two of its sub-regions : lateral and medial. We present a system-level computational model of decision-making and learning involving the two sub-regions taking into account their individual roles as commonly implicated in neuroscientific studies. We emphasize on the role of the interactions between the sub-regions within the OFC as well as the role of other sub-cortical structures which form a network with them. We leverage well-known computational architecture of thalamo-cortical basal ganglia loops, accounting for recent experimental findings on monkeys with lateral and medial OFC lesions, performing a 3-arm bandit task. First we replicate the seemingly dissociate effects of lesions to lateral and medial OFC during decision-making as a function of value-difference of the presented options. Further we demonstrate and argue that such an effect is not necessarily due to the dissociate roles of both the subregions, but rather a result of complex temporal dynamics between the interacting networks in which they are involved.Author summaryWe first highlight the role of the Orbitofrontal Cortex (OFC) in value-based decision making and goal-directed behavior in primates. We establish the position of OFC at the intersection of cortical mechanisms and thalamo-basal ganglial circuits. In order to understand possible mechanisms through which the OFC exerts emotional control over behavior, among several other possibilities, we consider the case of dissociate roles of two of its topographical subregions - lateral and medial parts of OFC. We gather predominant roles of each of these sub-regions as suggested by numerous experimental evidences in the form of a system-level computational model that is based on existing neuronal architectures. We argue that besides possible dissociation, there could be possible interaction of these sub-regions within themselves and through other sub-cortical structures, in distinct mechanisms of choice and learning. The computational framework described accounts for experimental data and can be extended to more comprehensive detail of representations required to understand the processes of decision-making, learning and the role of OFC and subsequently the regions of prefrontal cortex in general.

scholarly journals Image-to-Image Translation with Multi-Path Consistency Regularization

2019 ◽  
Author(s):  
Jianxin Lin ◽  
Yingce Xia ◽  
Yijun Wang ◽  
Tao Qin ◽  
Zhibo Chen
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
The Other ◽  
Natural Images ◽  
Original Image ◽  
Target Domain ◽  
Source Domain ◽  
Face To Face ◽  
Image Translation ◽  
The Difference

Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from a source domain to a target domain as an example, existing algorithms mainly rely on two kinds of loss for training: One is the discrimination loss, which is used to differentiate images generated by the models and natural images; the other is the reconstruction loss, which measures the difference between an original image and the reconstructed version. In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation from source domain to target domain and indirect translation from source domain to an auxiliary domain to target domain, to regularize training. For multi-domain translation (at least, three) which focuses on building translation models between any two domains, at each training iteration, we randomly select three domains, set them respectively as the source, auxiliary and target domains, build the multi-path consistency loss and optimize the network. For two-domain translation, we need to introduce an additional auxiliary domain and construct the multi-path consistency loss. We conduct various experiments to demonstrate the effectiveness of our proposed methods, including face-to-face translation, paint-to-photo translation, and de-raining/de-noising translation.

A cryptosystem for lossless/lossy grayscale images in IWT domain using chaotic map-based generated key matrices

2018 ◽  
Vol 16 (04) ◽  
pp. 1850024 ◽  
Author(s):  
Kshiramani Naik ◽  
Arup Kumar Pal
Keyword(s):  
Chaotic Map ◽  
Logistic Map ◽  
Security Analysis ◽  
Image Data ◽  
Natural Images ◽  
Security Level ◽  
Original Image ◽  
Data Set ◽  
The Individual ◽  
Chaotic Logistic Map

In this paper, an image encryption scheme based on reversible integer wavelet transform (IWT) with chaotic logistic map is designed. The proposed cryptosystem is applicable to encipher both the medical and natural images in lossless and lossy manners, respectively. Initially, the original image is transformed with the multilevel of IWT, then the image data set is divided into low sub-band (approximation part) and high sub-bands (detail part). The approximation part gets confused with the chaotic logistic map followed by the bit plane decomposition. Next, the individual bit planes are further diffused with several binary key metrics, generated using a chaotic logistic map. The proposed key schedule derives several large size of binary key metrics from a small size of key. Based on the type of applications, the detail part is considered for lossless/lossy compression. The lossless/lossy compressed detail part is further considered only for confusion process using the logistic map for the sake of enhancing the security level. Finally, the cipher image obtained after inverse IWT is significantly dissimilar than original image. The scheme has been tested on several standard medical and natural images and the experimental results substantiate that a small size of key is enough to protect the content of images completely. The security analysis reveals that the proposed scheme is suitable for protecting the image data effectively.

scholarly journals An oscillating computational model can track pseudo-rhythmic speech by using linguistic predictions

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Sanne ten Oever ◽  
Andrea E Martin
Keyword(s):  
Computational Model ◽  
Sensory Processing ◽  
Empirical Data ◽  
Temporal Dynamics ◽  
Language Models ◽  
Brain Oscillations ◽  
Neuronal Oscillations ◽  
Speech Input ◽  
Temporal Phase ◽  
Speech Time

Neuronal oscillations putatively track speech in order to optimize sensory processing. However, it is unclear how isochronous brain oscillations can track pseudo-rhythmic speech input. Here we propose that oscillations can track pseudo-rhythmic speech when considering that speech time is dependent on content-based predictions flowing from internal language models. We show that temporal dynamics of speech are dependent on the predictability of words in a sentence. A computational model including oscillations, feedback, and inhibition is able to track pseudo-rhythmic speech input. As the model processes, it generates temporal phase codes, which are a candidate mechanism for carrying information forward in time. The model is optimally sensitive to the natural temporal speech dynamics and can explain empirical data on temporal speech illusions. Our results suggest that speech tracking does not have to rely only on the acoustics but could also exploit ongoing interactions between oscillations and constraints flowing from internal language models.

scholarly journals Textures vs Non-Textures: A Simple Computational Method for Classifying Perceived ‘Texturality’ in Natural Images

i-Perception ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204166952110545
Author(s):  
Fumiya Kurosawa ◽  
Taiki Orima ◽  
Kosuke Okada ◽  
Isamu Motoyoshi
Keyword(s):  
Visual System ◽  
Computational Method ◽  
Natural Images ◽  
Strongly Correlated ◽  
Original Image ◽  
Perceived Similarity ◽  
Image Region ◽  
Discriminant Model ◽  
Small Set ◽  
Highly Correlated

The visual system represents textural image regions as simple statistics that are useful for the rapid perception of scenes and surfaces. What images ‘textures’ are, however, has so far mostly been subjectively defined. The present study investigated the empirical conditions under which natural images are processed as texture. We first show that ‘texturality’ – i.e., whether or not an image is perceived as a texture – is strongly correlated with the perceived similarity between an original image and its Portilla-Simoncelli (PS) synthesized image. We found that both judgments are highly correlated with specific PS statistics of the image. We also demonstrate that a discriminant model based on a small set of image statistics could discriminate whether a given image was perceived as a texture with over 90% accuracy. The results provide a method to determine whether a given image region is represented statistically by the human visual system.

Backscroll Illusion

2017 ◽  
Author(s):  
Kiyoshi Fujimoto
Keyword(s):  
Relative Motion ◽  
Translational Motion ◽  
Human Vision ◽  
Natural Images ◽  
Motion Pattern ◽  
Motion Representation ◽  
Object Based ◽  
High Level ◽  
Psychophysical Studies ◽  
Direction Opposite

Human vision recognizes the direction of a human, an animal, and objects in translational motion, even when they are displayed in a still position on a screen as filmed by a panning camera and with the background erased. Because there is no clue to relative motion between the object and the background, the recognition relies on a facing direction and/or movements of its internal parts like limbs. Such high-level object-based motion representation is capable of affecting lower-level motion perception. An ambiguous motion pattern is inserted to the screen behind the translating object. Then the pattern appears moving in a direction opposite to that which the object implies. This is called the backscroll illusion, and psychophysical studies were conducted to investigate phenomenal aspects with the hypothesis that the illusion reflects a strategy the visual system adopts in everyday circumstances. The backscroll illusion convincingly demonstrates that natural images contain visual illusions.

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