AIMC Topic: Recognition, Psychology

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ASNet: Auto-Augmented Siamese Neural Network for Action Recognition.

Sensors (Basel, Switzerland)
Human action recognition methods in videos based on deep convolutional neural networks usually use random cropping or its variants for data augmentation. However, this traditional data augmentation approach may generate many non-informative samples (...

A deep convolutional neural network model for hand gesture recognition in 2D near-infrared images.

Biomedical physics & engineering express
The hand gesture recognition (HGR) process is one of the most vital components in human-computer interaction systems. Especially, these systems facilitate hearing-impaired people to communicate with society. This study aims to design a deep learning ...

Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm.

Computational intelligence and neuroscience
In this paper, a deep confidence neural network algorithm is used to design and deeply analyze the risk warning model for stadium operation. Many factors, such as video shooting angle, background brightness, diversity of features, and the relationshi...

SAR ATR for Limited Training Data Using DS-AE Network.

Sensors (Basel, Switzerland)
Although automatic target recognition (ATR) with synthetic aperture radar (SAR) images has been one of the most important research topics, there is an inherent problem of performance degradation when the number of labeled SAR target images for traini...

First Step toward Gestural Recognition in Harsh Environments.

Sensors (Basel, Switzerland)
We are witnessing a rise in the use of ground and aerial robots in first response missions. These robots provide novel opportunities to support first responders and lower the risk to people's lives. As these robots become increasingly autonomous, res...

An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network.

Sensors (Basel, Switzerland)
Recently, deep learning approaches, especially convolutional neural networks (CNNs), have attracted extensive attention in iris recognition. Though CNN-based approaches realize automatic feature extraction and achieve outstanding performance, they us...

Learning to recognize while learning to speak: Self-supervision and developing a speaking motor.

Neural networks : the official journal of the International Neural Network Society
Traditionally, learning speech synthesis and speech recognition were investigated as two separate tasks. This separation hinders incremental development for concurrent synthesis and recognition, where partially-learned synthesis and partially-learned...

The Function of Color and Structure Based on EEG Features in Landscape Recognition.

International journal of environmental research and public health
Both color and structure make important contributions to human visual perception, as well as the evaluation of landscape quality and landscape aesthetics. The EEG equipment liveamp32 was used to record the EEG signals of humans when viewing landscape...

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos.

Sensors (Basel, Switzerland)
Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environ...

What Can Network Science Tell Us About Phonology and Language Processing?

Topics in cognitive science
Contemporary psycholinguistic models place significant emphasis on the cognitive processes involved in the acquisition, recognition, and production of language but neglect many issues related to the representation of language-related information in t...