Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Association Areas of the Cortex

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Obesity and Early Sepsis-Associated Acute Respiratory Distress Syndrome: A Prospective Multicenter Study.

Respiratory medicine·2026

Same author

Trajectories of Oxygenation Index and PEEP Levels Associated with 28-Day Mortality in Sepsis-Associated ARDS: A Multicenter Cohort Study.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2026

Same author

Deep learning and machine learning in image-based hepatocellular carcinoma detection: a systematic review and meta-analysis.

Abdominal radiology (New York)·2026

Same author

Molecular insights and therapeutic implications for the tumor suppressor role of LKB1 in non-small cell lung cancer.

Cancer letters·2026

Same author

When task performance deceives: Task-geometry decoupling in learnable-curvature hyperbolic GNNs.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Molecular insights for the tumor suppressor role of SPOP in prostate cancer.

Biochimica et biophysica acta. Reviews on cancer·2026

Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026

Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026

Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026

Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026

Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026

Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 5, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Fine-art recognition using convolutional transformers.

Yu Liu¹, Haozhe Bai¹, Jingchao Wang¹

¹School of Arts, Chongqing University, Chongqing, China.

Peerj. Computer Science

|December 9, 2024

Summary

This summary is machine-generated.

This study introduces an improved convolutional transformer system for fine-art painting recognition, enhancing art security. The new model significantly outperforms existing methods, demonstrating the effectiveness of convolutional transformers in image classification.

Keywords:

Deep learning Fine-art Painting Recognition Transfer learning Transformers

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 5, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Digital image processing
Computer vision
Deep learning

Background:

Digitalization enhances art accessibility and conservation but poses security risks like theft.
Precise identification of fine-art paintings is crucial for improving art security.
Current art recognition systems have limitations, requiring efficiency enhancements.

Purpose of the Study:

To develop an improved recognition system for categorizing fine-art paintings.
To enhance the efficiency of art identification using advanced deep learning techniques.
To leverage convolutional transformers with attention mechanisms for focused learning.

Main Methods:

Developed a novel recognition system using convolutional transformers with an attention mechanism.
Integrated transformers, a deep learning architecture known for multi-head attention, to improve learning efficiency.
Compared the proposed system against four pre-trained networks (ResNet50, VGG16, AlexNet, ViT) using transfer learning.

Main Results:

The proposed convolutional transformer system outperformed existing models, including those based on ResNet50, VGG16, AlexNet, and ViT.
Experimental results demonstrated superior performance in fine-art painting categorization.
Effectiveness of convolutional transformers for learning intricate image features was highlighted.

Conclusions:

The developed convolutional transformer system offers a significant improvement for fine-art painting recognition.
This approach enhances the potential for securing valuable artworks against theft.
Convolutional transformers are highly effective for image feature learning in specialized domains like art analysis.