scholarly journals Contributions of low- and high-level properties to neural processing of visual scenes in the human brain

2017 ◽  
Vol 372 (1714) ◽  
pp. 20160102 ◽  
Author(s):  
Iris I. A. Groen ◽  
Edward H. Silson ◽  
Chris I. Baker
Keyword(s):  
Temporal Dynamics ◽  
Scene Perception ◽  
Visual Scene ◽  
Scene Analysis ◽  
Extrastriate Cortex ◽  
Low Level ◽  
Feature Representations ◽  
Scene Representation ◽  
Scene Processing ◽  
High Level

Visual scene analysis in humans has been characterized by the presence of regions in extrastriate cortex that are selectively responsive to scenes compared with objects or faces. While these regions have often been interpreted as representing high-level properties of scenes (e.g. category), they also exhibit substantial sensitivity to low-level (e.g. spatial frequency) and mid-level (e.g. spatial layout) properties, and it is unclear how these disparate findings can be united in a single framework. In this opinion piece, we suggest that this problem can be resolved by questioning the utility of the classical low- to high-level framework of visual perception for scene processing, and discuss why low- and mid-level properties may be particularly diagnostic for the behavioural goals specific to scene perception as compared to object recognition. In particular, we highlight the contributions of low-level vision to scene representation by reviewing (i) retinotopic biases and receptive field properties of scene-selective regions and (ii) the temporal dynamics of scene perception that demonstrate overlap of low- and mid-level feature representations with those of scene category. We discuss the relevance of these findings for scene perception and suggest a more expansive framework for visual scene analysis. This article is part of the themed issue ‘Auditory and visual scene analysis’.

scholarly journals Auditory and visual scene analysis: an overview

2017 ◽  
Vol 372 (1714) ◽  
pp. 20160099 ◽  
Author(s):  
Hirohito M. Kondo ◽  
Anouk M. van Loon ◽  
Jun-Ichiro Kawahara ◽  
Brian C. J. Moore
Keyword(s):  
Scene Perception ◽  
Computational Modelling ◽  
Visual Scene ◽  
Scene Analysis ◽  
Theoretical Approaches ◽  
Visual Inputs ◽  
Sensory Modalities ◽  
Stimulus Features ◽  
The Brain ◽  
Selection Of

We perceive the world as stable and composed of discrete objects even though auditory and visual inputs are often ambiguous owing to spatial and temporal occluders and changes in the conditions of observation. This raises important questions regarding where and how ‘scene analysis’ is performed in the brain. Recent advances from both auditory and visual research suggest that the brain does not simply process the incoming scene properties. Rather, top-down processes such as attention, expectations and prior knowledge facilitate scene perception. Thus, scene analysis is linked not only with the extraction of stimulus features and formation and selection of perceptual objects, but also with selective attention, perceptual binding and awareness. This special issue covers novel advances in scene-analysis research obtained using a combination of psychophysics, computational modelling, neuroimaging and neurophysiology, and presents new empirical and theoretical approaches. For integrative understanding of scene analysis beyond and across sensory modalities, we provide a collection of 15 articles that enable comparison and integration of recent findings in auditory and visual scene analysis. This article is part of the themed issue ‘Auditory and visual scene analysis’.

scholarly journals TMS to the Lateral Occipital Cortex Disrupts Object Processing but Facilitates Scene Processing

2011 ◽  
Vol 23 (12) ◽  
pp. 4174-4184 ◽  
Author(s):  
Caitlin R. Mullin ◽  
Jennifer K. E. Steeves
Keyword(s):  
Temporal Dynamics ◽  
Scene Perception ◽  
Visual Image ◽  
Object Perception ◽  
Occipital Cortex ◽  
Brain Regions ◽  
Object Processing ◽  
Repetitive Tms ◽  
Scene Processing ◽  
Lateral Occipital Cortex

The study of brain-damaged patients and advancements in neuroimaging have lead to the discovery of discrete brain regions that process visual image categories, such as objects and scenes. However, how these visual image categories interact remains unclear. For example, is scene perception simply an extension of object perception, or can global scene “gist” be processed independently of its component objects? Specifically, when recognizing a scene such as an “office,” does one need to first recognize its individual objects, such as the desk, chair, lamp, pens, and paper to build up the representation of an “office” scene? Here, we show that temporary interruption of object processing through repetitive TMS to the left lateral occipital cortex (LO), an area known to selectively process objects, impairs object categorization but surprisingly facilitates scene categorization. This result was replicated in a second experiment, which assessed the temporal dynamics of this disruption and facilitation. We further showed that repetitive TMS to left LO significantly disrupted object processing but facilitated scene processing when stimulation was administered during the first 180 msec of the task. This demonstrates that the visual system retains the ability to process scenes during disruption to object processing. Moreover, the facilitation of scene processing indicates disinhibition of areas involved in global scene processing, likely caused by disrupting inhibitory contributions from the LO. These findings indicate separate but interactive pathways for object and scene processing and further reveal a network of inhibitory connections between these visual brain regions.

scholarly journals The rhythms of predictive coding: pre-stimulus phase modulates the influence of shape perception on luminance judgments

2016 ◽  
Author(s):  
Biao Han ◽  
Rufin VanRullen
Keyword(s):  
Temporal Dynamics ◽  
Human Subjects ◽  
Predictive Coding ◽  
Shape Perception ◽  
Low Level ◽  
3D Shape ◽  
Coding Efficiency ◽  
Spatial Response ◽  
Left And Right ◽  
High Level

AbstractPredictive coding is an influential model emphasizing interactions between feedforward and feedback signals. Here, we investigated its temporal dynamics. Two gray disks with different versions of the same stimulus, one enabling predictive feedback (a 3D-shape) and one impeding it (random-lines), were simultaneously presented on the left and right of fixation. Human subjects judged the luminance of the two disks while EEG was recorded. Independently of the spatial response (left/right), we found that the choice of 3D-shape or random-lines as the brighter disk (our measure of post-stimulus predictive coding efficiency on each trial) fluctuated along with the pre-stimulus phase of two spontaneous oscillations: a ~5Hz oscillation in contralateral frontal electrodes and a ~16Hz oscillation in contralateral occipital electrodes. This pattern of results demonstrates that predictive coding is a rhythmic process, and suggests that it could take advantage of faster oscillations in low-level areas and slower oscillations in high-level areas.

Synergetic Multi-Semantic Multi-Instance Learning for Scene Recognition

2012 ◽  
Vol 220-223 ◽  
pp. 2188-2191
Author(s):  
Wen Gang Feng ◽  
Xue Chen
Keyword(s):  
Vision System ◽  
Scene Recognition ◽  
Human Vision ◽  
Human Vision System ◽  
Probabilistic Framework ◽  
Scene Classification ◽  
Related Factors ◽  
Low Level ◽  
Scene Representation ◽  
High Level

In this paper, the problem of scene representation is modeled by simultaneously considering the stimulus-driven and instance-related factors in a probabilistic framework. In this framework, a stimulus-driven component simulates the low-level processes in human vision system using semantic constrain; while a instance-related component simulate the high-level processes to bias the competition of the input features. We interpret the synergetic multi-semantic multi-instance learning on five scene database of LabelMe benchmark, and validate scene classification on the fifteen scene database via the SVM inference with comparison to the state-of-arts methods.

Visual Scene Processing in Familiar and Unfamiliar Environments

2007 ◽  
Vol 97 (5) ◽  
pp. 3670-3683 ◽  
Author(s):  
Russell A. Epstein ◽  
J. Stephen Higgins ◽  
Karen Jablonski ◽  
Alana M. Feiler
Keyword(s):  
Spatial Information ◽  
Scene Perception ◽  
Retrosplenial Cortex ◽  
Visual Scene ◽  
Neural Systems ◽  
Repetition Suppression ◽  
Neural Representations ◽  
Parahippocampal Cortex ◽  
Scene Processing ◽  
Viewpoint Invariance

Humans and animals use information obtained from the local visual scene to orient themselves in the wider world. Although neural systems involved in scene perception have been identified, the extent to which processing in these systems is affected by previous experience is unclear. We addressed this issue by scanning subjects with functional magnetic resonance imaging (fMRI) while they viewed photographs of familiar and unfamiliar locations. Scene-selective regions in parahippocampal cortex (the parahippocampal place area, or PPA), retrosplenial cortex (RSC), and the transverse occipital sulcus (TOS) responded more strongly to images of familiar locations than to images of unfamiliar locations with the strongest effects (>50% increase) in RSC. Examination of fMRI repetition suppression (RS) effects indicated that images of familiar and unfamiliar locations were processed with the same degree of viewpoint specificity; however, increased viewpoint invariance was observed as individual scenes became more familiar over the course of a scan session. Surprisingly, these within-scan-session viewpoint-invariant RS effects were only observed when scenes were repeated across different trials but not when scenes were repeated within a trial, suggesting that within- and between-trial RS effects may index different aspects of visual scene processing. The sensitivity to environmental familiarity observed in the PPA, RSC, and TOS supports earlier claims that these regions mediate the extraction of navigationally relevant spatial information from visual scenes. As locations become familiar, the neural representations of these locations become enriched, but the viewpoint invariance of these representations does not change.

scholarly journals Connecting Low-Level Image Processing and High-Level Vision via Deep Learning

2018 ◽  
Author(s):  
Ding Liu
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Learning Mechanisms ◽  
Daily Lives ◽  
Low Level ◽  
Feature Representations ◽  
Technological Advances ◽  
High Level ◽  
Tremendous Impact

The latest development of computer vision has made exciting progress and tremendous impact in our daily lives. In this exciting era of technological advances, deep learning has gained huge popularity as a powerful tool for solving a lot of computer vision problems, and has added a great boost to this already rapidly developing field. Conventionally, the connection between different vision tasks is fragile. For example, low-level image processing and high-level vision tasks are usually coped with separately. However, the inherent relation of feature representations among various tasks should be effectively utilized rather than omitted. My research focuses on connecting low-level image processing and high-level vision via deep learning. Specifically, my goal is to design deep learning mechanisms that can efficiently and effectively learn features from low-level image processing and use them to improve the performance of high-level vision tasks.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

Determinants of Abuse of the Bodies of the Dead

2019 ◽  
pp. 71-77
Author(s):  
Margarita Khomyakova
Keyword(s):  
Low Income ◽  
Criminal Code ◽  
Value Judgments ◽  
Legal Norms ◽  
Low Level ◽  
Medical Institutions ◽  
Specific Object ◽  
The Subject ◽  
The Dead ◽  
High Level

The author analyzes definitions of the concepts of determinants of crime given by various scientists and offers her definition. In this study, determinants of crime are understood as a set of its causes, the circumstances that contribute committing them, as well as the dynamics of crime. It is noted that the Russian legislator in Article 244 of the Criminal Code defines the object of this criminal assault as public morality. Despite the use of evaluative concepts both in the disposition of this norm and in determining the specific object of a given crime, the position of criminologists is unequivocal: crimes of this kind are immoral and are in irreconcilable conflict with generally accepted moral and legal norms. In the paper, some views are considered with regard to making value judgments which could hardly apply to legal norms. According to the author, the reasons for abuse of the bodies of the dead include economic problems of the subject of a crime, a low level of culture and legal awareness; this list is not exhaustive. The main circumstances that contribute committing abuse of the bodies of the dead and their burial places are the following: low income and unemployment, low level of criminological prevention, poor maintenance and protection of medical institutions and cemeteries due to underperformance of state and municipal bodies. The list of circumstances is also open-ended. Due to some factors, including a high level of latency, it is not possible to reflect the dynamics of such crimes objectively. At the same time, identification of the determinants of abuse of the bodies of the dead will reduce the number of such crimes.

scholarly journals EXPRESS: Marketers Project Their Personal Preferences onto Consumers: Overcoming the Threat of Egocentric Decision Making

2021 ◽  
pp. 002224372199837
Author(s):  
Walter Herzog ◽  
Johannes D. Hattula ◽  
Darren W. Dahl
Keyword(s):  
Decision Making ◽  
Consumer Preferences ◽  
Research Question ◽  
Pilot Studies ◽  
Low Level ◽  
Marketing Managers ◽  
False Consensus Effect ◽  
False Consensus ◽  
High Level

This research explores how marketing managers can avoid the so-called false consensus effect—the egocentric tendency to project personal preferences onto consumers. Two pilot studies were conducted to provide evidence for the managerial importance of this research question and to explore how marketing managers attempt to avoid false consensus effects in practice. The results suggest that the debiasing tactic most frequently used by marketers is to suppress their personal preferences when predicting consumer preferences. Four subsequent studies show that, ironically, this debiasing tactic can backfire and increase managers’ susceptibility to the false consensus effect. Specifically, the results suggest that these backfire effects are most likely to occur for managers with a low level of preference certainty. In contrast, the results imply that preference suppression does not backfire but instead decreases false consensus effects for managers with a high level of preference certainty. Finally, the studies explore the mechanism behind these results and show how managers can ultimately avoid false consensus effects—regardless of their level of preference certainty and without risking backfire effects.

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