A Comprehensive Review of Modern Object Segmentation Approaches

DOI10.1561/0600000097OpenAlexW4312781473MaRDI QIDQ5046687

Yuanbo Wang, Unaiza Ahsan, Hanyan Li, Matthew Hagen

Publication date: 9 November 2022

Published in: Foundations and Trends® in Computer Graphics and Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1561/0600000097

zbMATH Keywords

deep learningimage and video processingobject and scene recognitionfeature detection and selectionrobot localization and navigationsegmentation and groupingsensors and sensingvideo analysis and event recognition

Mathematics Subject Classification ID

Computer science (68-XX)

Uses Software

• DeepLab
• GrabCut
• ImageNet
• GitHub
• Tensor2Tensor
• SSD
• SegNet
• KITTI
• MS-COCO
• U-Net
• YOLACT++
• LVIS
• Point2Sequence
• AlexNet
• NYU Depth
• Panoptic-DeepLab
• CCNet
• EfficientPS
• ViP-DeepLab
• UPSNet
• DeeperLab
• Axial-DeepLab
• MaX-DeepLab
• PolarNet
• RefineNet
• Swin Transformer
• ViT
• Deformable DETR
• Faster R-CNN

Cites Work

• U-Net
• Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
• OCNet: object context for semantic segmentation
• Optimal approximations by piecewise smooth functions and associated variational problems
• Active contours without edges
• Convexity Shape Prior for Level Set-Based Image Segmentation Method
• Unnamed Item
• Unnamed Item

This page was built for publication: A Comprehensive Review of Modern Object Segmentation Approaches