The visual recognition and detection of emotional body postures

2010 ◽  
Author(s):  
Ashley Blanchard ◽  
Maggie Shiffrar
Keyword(s):  
Visual Recognition ◽  
Body Postures

Do You Want to See the Tree? Ignore the Forest

2014 ◽  
Vol 61 (3) ◽  
pp. 205-214 ◽  
Author(s):  
Nicolas Poirel ◽  
Claire Sara Krakowski ◽  
Sabrina Sayah ◽  
Arlette Pineau ◽  
Olivier Houdé ◽  
...  
Keyword(s):  
Visual Recognition ◽  
Local Level ◽  
Local Information ◽  
Visual Environment ◽  
Global Information ◽  
Focus Attention ◽  
Global Level ◽  
Hierarchical Stimuli ◽  
Priming Paradigm ◽  
The One

The visual environment consists of global structures (e.g., a forest) made up of local parts (e.g., trees). When compound stimuli are presented (e.g., large global letters composed of arrangements of small local letters), the global unattended information slows responses to local targets. Using a negative priming paradigm, we investigated whether inhibition is required to process hierarchical stimuli when information at the local level is in conflict with the one at the global level. The results show that when local and global information is in conflict, global information must be inhibited to process local information, but that the reverse is not true. This finding has potential direct implications for brain models of visual recognition, by suggesting that when local information is conflicting with global information, inhibitory control reduces feedback activity from global information (e.g., inhibits the forest) which allows the visual system to process local information (e.g., to focus attention on a particular tree).

Mechanisms for precuing superiority in visual recognition.

1982 ◽  
Vol 8 (1) ◽  
pp. 58-67
Author(s):  
Dean E. Stolldorf ◽  
Gordon M. Redding ◽  
Leon M. Manelis
Keyword(s):  
Visual Recognition

Domain Adaptation for Visual Recognition

2015 ◽  
Author(s):  
Raghuraman Gopalan ◽  
Ruonan Li ◽  
Vishal M. Patel ◽  
Rama Chellappa
Keyword(s):  
Visual Recognition ◽  
Domain Adaptation

Featural and Configural Processing of Faces and Houses in Matched Dyslexic and Typical Readers

2020 ◽  
Author(s):  
Bahareh Jozranjbar ◽  
Arni Kristjansson ◽  
Heida Maria Sigurdardottir
Keyword(s):  
Object Recognition ◽  
Visual Processing ◽  
Reading Ability ◽  
Visual Recognition ◽  
Reading Deficits ◽  
Domain Specific ◽  
Configural Information ◽  
Lower House ◽  
Single Process ◽  
Processing Mechanisms

While dyslexia is typically described as a phonological deficit, recent evidence suggests that ventral stream regions, important for visual categorization and object recognition, are hypoactive in dyslexic readers who might accordingly show visual recognition deficits. By manipulating featural and configural information of faces and houses, we investigated whether dyslexic readers are disadvantaged at recognizing certain object classes or utilizing particular visual processing mechanisms. Dyslexic readers found it harder to recognize objects (houses), suggesting that visual problems in dyslexia are not completely domain-specific. Mean accuracy for faces was equivalent in the two groups, compatible with domain-specificity in face processing. While face recognition abilities correlated with reading ability, lower house accuracy was nonetheless related to reading difficulties even when accuracy for faces was kept constant, suggesting a specific relationship between visual word recognition and the recognition of non-face objects. Representational similarity analyses (RSA) revealed that featural and configural processes were clearly separable in typical readers, while dyslexic readers appeared to rely on a single process. This occurred for both faces and houses and was not restricted to particular visual categories. We speculate that reading deficits in some dyslexic readers reflect their reliance on a single process for object recognition.

Visual Recognition of Continuous Cued Speech Using a Tandem CNN-HMM Approach

2018 ◽  
Author(s):  
Li Liu ◽  
Thomas Hueber ◽  
Gang Feng ◽  
Denis Beautemps
Keyword(s):  
Visual Recognition ◽  
Cued Speech

Applying Deep Learning to Audit Procedures: An Illustrative Framework

2019 ◽  
Vol 33 (3) ◽  
pp. 89-109 ◽  
Author(s):  
Ting (Sophia) Sun
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Data Warehouse ◽  
Visual Recognition ◽  
Prediction Models ◽  
Historical Data ◽  
Structured Data ◽  
Text Understanding ◽  
Audit Data ◽  
Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

Research on visual recognition intelligent system of city brand image based on fuzzy theory and regional culture

2021 ◽  
pp. 1-9
Author(s):  
Lin Han ◽  
Lu Han
Keyword(s):  
Neural Network ◽  
Visual Recognition ◽  
Intelligent System ◽  
Brand Image ◽  
Fuzzy Theory ◽  
Recognition System ◽  
Market Competitiveness ◽  
Visual Identity ◽  
Fuzzy Neural ◽  
Intelligent Recognition

With the rapid development of China’s market economy, brand image is becoming more and more important for an enterprise to enhance its market competitiveness and occupy a favorable market share. However, the brand image of many established companies gradually loses with the development of society and the improvement of people’s aesthetic pursuit. This has forced it to change its corporate brand image and regain the favor of the market. Based on this, this article combines the related knowledge and concepts of fuzzy theory, from the perspective of visual identity design, explores the development of corporate brand image visual identity intelligent system, and aims to design a set of visual identity system that is different from competitors in order to shape the enterprise. Distinctive brand image and improve its market competitiveness. This article first collected a large amount of information through the literature investigation method, and made a systematic and comprehensive introduction to fuzzy theory, visual recognition technology and related theoretical concepts of brand image, which laid a sufficient theoretical foundation for the later discussion of the application of fuzzy theory in the design of brand image visual recognition intelligent system; then the fuzzy theory algorithm is described in detail, a fuzzy neural network is proposed and applied to the design of the brand image visual recognition intelligent system, and the design experiment of the intelligent recognition system is carried out; finally, through the use of the specific case of KFC brand logo, the designed intelligent recognition system was tested, and it was found that the visual recognition intelligent system had an overall accuracy rate of 96.08% for the KFC brand logo. Among them, the accuracy rate of color recognition was the highest, 96.62%; comparing the changes in the output value of the training sample and the test sample, the output convergence effect of the color network is the best; through the comparison test of the BP neural network, the recognition effect of the fuzzy neural network is better.

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