scholarly journals Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1169
Author(s):  
Bardia Yousefi ◽  
Chu Kiong Loo
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Activity Recognition ◽  
Computational Models ◽  
Neural Mechanism ◽  
Human Action ◽  
Action Perception ◽  
Biologically Inspired ◽  
Knowledge Based ◽  
Massive Information

Theoretical neuroscience investigation shows valuable information on the mechanism for recognizing the biological movements in the mammalian visual system. This involves many different fields of researches such as psychological, neurophysiology, neuro-psychological, computer vision, and artificial intelligence (AI). The research on these areas provided massive information and plausible computational models. Here, a review on this subject is presented. This paper describes different perspective to look at this task including action perception, computational and knowledge based modeling, psychological, and neuroscience approaches.

scholarly journals The minimalist grammar of action

2012 ◽  
Vol 367 (1585) ◽  
pp. 103-117 ◽  
Author(s):  
Katerina Pastra ◽  
Yiannis Aloimonos
Keyword(s):  
Computational Models ◽  
Reference Model ◽  
Action Learning ◽  
Human Action ◽  
Human Cognition ◽  
Neural Basis ◽  
Biologically Inspired ◽  
Language Structure ◽  
Minimalist Grammar ◽  
Temporal Sequences

Language and action have been found to share a common neural basis and in particular a common ‘syntax’, an analogous hierarchical and compositional organization. While language structure analysis has led to the formulation of different grammatical formalisms and associated discriminative or generative computational models, the structure of action is still elusive and so are the related computational models. However, structuring action has important implications on action learning and generalization, in both human cognition research and computation. In this study, we present a biologically inspired generative grammar of action, which employs the structure-building operations and principles of Chomsky's Minimalist Programme as a reference model. In this grammar, action terminals combine hierarchically into temporal sequences of actions of increasing complexity; the actions are bound with the involved tools and affected objects and are governed by certain goals. We show, how the tool role and the affected-object role of an entity within an action drives the derivation of the action syntax in this grammar and controls recursion, merge and move, the latter being mechanisms that manifest themselves not only in human language, but in human action too.

Image Feature Extraction and Object Recognition Based on Vision Neural Mechanism

2019 ◽  
Vol 34 (06) ◽  
pp. 2054017 ◽  
Author(s):  
Peng Cheng Wei ◽  
Yang Zou
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Object Recognition ◽  
Visual Information ◽  
Neural Mechanism ◽  
Image Features ◽  
Image Feature ◽  
Training Samples ◽  
Computer Vision Technology ◽  
Processing Of Visual Information

As an important branch of artificial intelligence, computer vision plays a huge role in the rapid development of artificial intelligence. From a biological point of view, in the acquisition and processing of information, vision is much more important than hearing, touch, etc., because 70% of the human cerebral cortex is processing visual information. Therefore, advances in computer vision technology are critical to the development of artificial intelligence that is designed to allow machines to think and handle things like humans. The acquisition and processing of visual information has always been the focus of computer vision research, and it is also difficult. The main problem of traditional computer vision technology in the processing of visual information is that the extracted image features are less discriminative, the generalization ability of image features in complex background scenes is insufficient, and the recognition ability on object recognition is poor. In response to these problems, based on the visual neural mechanism, this paper establishes an appropriate computer model for the neuronal cells in the human primary visual cortex, models the recognition response mechanism of the visual ventral system, and performs image feature extraction on the training samples. And object recognition. The results show that compared with the traditional methods, the proposed method effectively improves the discrimination of image features, and the image features extracted under complex background scenes have good generalization ability. On this basis, the training samples can be effectively recognized. The results show that the model based on the visual neural mechanism, the recognition of the edge, orientation and contour of the training sample show the advantages of the biological vision mechanism in object recognition.

scholarly journals Deep Neural Network Concepts for Classification using Convolutional Neural Network: A Systematic Review and Evaluation

2021 ◽  
Vol 3 (8) ◽  
pp. 58-70
Author(s):  
Mohammad Galety
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Computational Models ◽  
Mathematical Object ◽  
Correct Selection ◽  
Covering Arrays ◽  
Biologically Inspired ◽  
Learning Tasks ◽  
Selection Of

In recent years, artificial intelligence (AI) has piqued the curiosity of researchers. Convolutional Neural Networks (CNN) is a deep learning (DL) approach commonly utilized to solve problems. In standard machine learning tasks, biologically inspired computational models surpass prior types of artificial intelligence by a considerable margin. The Convolutional Neural Network (CNN) is one of the most stunning types of ANN architecture. The goal of this research is to provide information and expertise on many areas of CNN. Understanding the concepts, benefits, and limitations of CNN is critical for maximizing its potential to improve image categorization performance.  This article has integrated the usage of a mathematical object called covering arrays to construct the set of ideal parameters for neural network design due to the complexity of the tuning process for the correct selection of the parameters used for this form of neural network.

Vision and Sensor-Based Human Activity Recognition

2020 ◽  
pp. 17-35 ◽  
Author(s):  
Md Atiqur Rahman Ahad
Keyword(s):  
Computer Vision ◽  
Activity Recognition ◽  
Affective Computing ◽  
Intelligent System ◽  
Human Action Recognition ◽  
Human Action ◽  
Important Research ◽  
Healthcare Facilities ◽  
The Core ◽  
Research Areas

Human action or activity or behavior analysis, recognition, and understanding are very important research areas in the field of computer vison, internet of things (IoT) sensor-based analysis, human-computer interaction (HCI), affective computing, intelligent system, healthcare facilities, and so on. There is much importance in human action recognition. This chapter introduces the core aspects of human action or activity recognition (HAR). These are split into two different domains: computer vision-based action/activity recognition, action localization, etc.; and wearable IoT sensor-based HAR. Though cameras are also sensors that provide vision-based information, the author puts camera-based or vision-based methods in another category. The chapter introduces core challenges and strategies to move forward.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

scholarly journals Out of the loop? On the radical and the routine in urban big data

Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Computational Models ◽  
Data Driven ◽  
Urban Systems ◽  
Out Of The Loop ◽  
Urban Futures ◽  
Urban Behaviour ◽  
The City

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

scholarly journals A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

2021 ◽  
Author(s):  
Daniel Auge ◽  
Julian Hille ◽  
Etienne Mueller ◽  
Alois Knoll
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Industrial Applications ◽  
Spiking Neural Networks ◽  
Biologically Inspired ◽  
Power Efficient ◽  
Signal Encoding ◽  
Uniform Nomenclature ◽  
Theoretical Foundations ◽  
Do So

AbstractBiologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.

scholarly journals ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 146876-146886
Author(s):  
Claudio Bettini ◽  
Gabriele Civitarese ◽  
Davide Giancane ◽  
Riccardo Presotto
Keyword(s):  
Activity Recognition ◽  
Data Driven ◽  
Knowledge Based ◽  
Probabilistic Knowledge ◽  
Hybrid Data
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