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,...

You might also read

Related Articles

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

Sort by

Same author

Latent topic-driven cyber intelligence model for tactics, techniques, and procedures (TTPs) detection using hybrid framework and Birch-inspired optimisation.

Scientific reports·2025

Same author

Skin lesion classification using multi-resolution empirical mode decomposition and local binary pattern.

PloS one·2022

Same author

A tree-based multiclassification of breast tumor histopathology images through deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2021

Same author

CMF-iteMS: An automatic threshold selection for detection of copy-move forgery.

Forensic science international·2018

Same author

Feature-Based Retinal Image Registration Using D-Saddle Feature.

Journal of healthcare engineering·2017

Same author

Blind source computer device identification from recorded VoIP calls for forensic investigation.

Forensic science international·2017

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: Jul 4, 2026

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Published on: April 10, 2016

An image quality assessment algorithm based on 'global + local' feature fusion.

Yang Yang¹, Norisma Binti Idris¹, Ainuddin Wahid Abdul Wahab¹

¹Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.

Peerj. Computer Science

|September 24, 2025

Summary

This summary is machine-generated.

This study introduces an image quality assessment algorithm (IQA-GL) that fuses global and local features. The novel approach improves image quality analysis by considering feature interactions and regional relationships.

Keywords:

Convolutional networks Feature channels Full-reference Global-local feature fusion Hierarchical perception mechanism Human visual requirements Image feature extraction Image quality assessment Structural similarity index Subjective biases

More Related Videos

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Published on: January 17, 2025

Related Experiment Videos

Last Updated: Jul 4, 2026

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Published on: April 10, 2016

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Published on: January 17, 2025

Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Current image quality assessment methods often use simplistic feature extraction, leading to underutilization of image data.
Existing approaches frequently overlook the crucial correlations between different image regions, limiting assessment accuracy.

Purpose of the Study:

To propose a novel image quality assessment algorithm, termed IQA-GL, that addresses limitations in current feature extraction and regional correlation analysis.
To enhance the extraction and utilization of image quality information by integrating global and local feature representations.

Main Methods:

Extraction of distinct global and local image features, with filtering of irrelevant local information.
Development of a global-local feature fusion model to improve feature interaction and aggregate quality data across all channels.
Modeling the relationship between image patches and the global image to dynamically adjust patch weights for a comprehensive quality score.

Main Results:

The proposed IQA-GL algorithm demonstrated excellent performance on established public datasets.
The fusion of global and local features significantly improved the accuracy and comprehensiveness of image quality assessment.
The method effectively captures inter-regional dependencies crucial for accurate image quality evaluation.

Conclusions:

The IQA-GL algorithm offers a significant advancement in image quality assessment by innovatively combining global and local features.
This approach provides a new perspective for analyzing image quality, emphasizing feature interaction and regional relationships.
The study highlights the potential of integrated feature fusion for more robust and accurate image quality evaluation systems.