ItemBook Chapter

Role of Graph Convolutional Neural Networks (GCNN) in Computer Vision Applications

Title
Role of Graph Convolutional Neural Networks (GCNN) in Computer Vision Applications
Creator
Malini, A.; Sharma, Vandana; Felicia Lilian, J.; Dhanaraj, Rajesh Kumar; Sharangapriyan, S.; Shrinivas, S.
Description
Graph Convolution Neural Networks (GCNNs) are an important concept in advancing computer vision by transforming the understanding and modeling of graph-structured data. They have a unique capability to capture intricate relations along with the visual content that goes beyond the traditional and usual convolutional neural networks, it also empowers computers to observe and interpret the complex interconnection between the elements in images, which enhances the depth and nuance of visual dentata analysis. As a revolutionary study in computer vision, GCNNs are poised to transform various industries by unleashing new frontiers in the visual information domains analysis and interpretation. Their multifaceted applications promise to reshape the landscape of computer vision. 2026 Scrivener Publishing LLC.
Source
Graph Convolutional Neural Networks for Computer Vision;pp.1-20
Date
01-01-2025
Publisher
wiley
Subject
computer vision; Graph convolutional neural networks (GCNNs); graph-structured data; visual data analysis; visual information analysis
Coverage
Malini A., School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, Chennai, India; Sharma V., Christ University, Bengaluru, India; Felicia Lilian J., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, India; Dhanaraj R.K., Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India; Sharangapriyan S., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, India; Shrinivas S., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-139435636-2; 978-139435633-1;
Format
online
Language
English
Type
Book chapter

Citation

Malini, A.; Sharma, Vandana; Felicia Lilian, J.; Dhanaraj, Rajesh Kumar; Sharangapriyan, S.; Shrinivas, S., “Role of Graph Convolutional Neural Networks (GCNN) in Computer Vision Applications,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23943.

Output Formats