Search Results - "Multi-class object segmentation"

Multi-class object segmentation based on jointly integrating segment-level and image-level object priors

Authors: Tuozhong Yao Jiatao Song Liangxu Liu et al.

Source: 2016 IEEE International Conference on Information and Automation (ICIA). :1770-1775

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://dblp.uni-trier.de/db/conf/icinfa/icinfa2016.html#YaoSAL16
https://ieeexplore.ieee.org/document/7832104/
http://ieeexplore.ieee.org/document/7832104/

An encoder-decoder deep learning method for multi-class object segmentation from 3D tunnel point clouds

Authors: Ankang Ji Alvin Wei Ze Chew Xiaolong Xue et al.

Source: Automation in Construction. 137:104187

Subject Terms: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Region based level sets for image segmentation: a brief comparative review with a fast model FREEST.

Authors: Feng, Chaolu Chen, Shuaizheng Zhao, Dazhe et al.

Source: Multimedia Tools & Applications; Oct2023, Vol. 82 Issue 24, p37065-37095, 31p

Object Segmentation for Autonomous Driving Using iseAuto Data.

Authors: Gu, Junyi Bellone, Mauro Sell, Raivo et al.

Source: Electronics (2079-9292); Apr2022, Vol. 11 Issue 7, p1119-N.PAG, 17p

Subject Terms: AUTONOMOUS vehicles, CONVOLUTIONAL neural networks, SHUTTLE services, DRIVERLESS cars, ROBUST optimization

Extraction and location of subway shield tunnel segment joints from RMLS point clouds.

Authors: Wang, Liying You, Ze Feng, Yong et al.

Source: International Journal of Digital Earth; Dec2025, Vol. 18 Issue 1, p1-28, 28p

Subject Terms: SUBWAY tunnels, POINT cloud, SUBWAYS, CURVATURE measurements, SAFETY, IMAGE segmentation, LIDAR

Scan-to-EDTs: Automated Generation of Energy Digital Twins from 3D Point Clouds.

Authors: Roman, Oscar Bassier, Maarten Agugiaro, Giorgio et al.

Source: Buildings (2075-5309); Nov2025, Vol. 15 Issue 22, p4060, 29p

Subject Terms: DIGITAL twin, ENERGY management, POINT cloud, ENERGY consumption, INTELLIGENT buildings, DEEP learning

Semi-BSU: A Boundary-Aware Semi-Supervised Semantic Segmentation Framework with Superpixel Refinement for Coastal Aquaculture Pond Extraction from Remote Sensing Images.

Authors: Gan, Yaocan Cheng, Bo Li, Chunbo et al.

Source: Remote Sensing; Nov2025, Vol. 17 Issue 22, p3733, 23p

Subject Terms: REMOTE sensing, IMAGE segmentation, IMAGE analysis, SUPERVISED learning, FISH ponds

A Few‐Shot Learning‐Based Point Cloud Semantic Segmentation Network for Tunnel Lining Inspection.

Authors: Li, Ziyi Jiang, Nan Tong, Lihong et al.

Source: IET Signal Processing (Wiley-Blackwell); 5/12/2025, p1-16, 16p

Subject Terms: TUNNEL lining, 6G networks, POINT cloud, INFRASTRUCTURE (Economics), STRUCTURAL stability, TUNNELS

Training-free Semantic Segmentation of Shield Tunnel Point Clouds.

Authors: Wang, Qiushi Qiao, Yafei Khoshelham, Kourosh et al.

Source: ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences; 4/6/2025, Vol. 10 Issue G-2025, p945-952, 8p

Robust Higher Order Potentials for Enforcing Label Consistency.

Authors: Kohli, Pushmeet Ladický, L’ubor Torr, Philip

Source: International Journal of Computer Vision; May2009, Vol. 82 Issue 3, p302-324, 23p, 11 Color Photographs, 1 Diagram, 6 Graphs

Subject Terms: COMPUTER vision, ARTIFICIAL intelligence, PARTIAL differential equations, RANDOM fields, MARKOV processes, PATTERN recognition systems, PROBLEM solving, IMAGE processing, PIXELS

A Review of Intelligent Depth Distance Perception Research for Power Transmission Line Corridor Scenarios.

Authors: Zhang, Jiaxin Wang, Bo Ma, Hengrui et al.

Source: Processes; Nov2024, Vol. 12 Issue 11, p2392, 25p

Subject Terms: ELECTRIC lines, DEPTH perception, SPACE perception, COMPLEX variables, POWER resources, SMART power grids, DEEP learning

A dual attentional skip connection based Swin‐UNet for real‐time cloud segmentation.

Authors: Wei, Fuhao Wang, Shaofan Sun, Yanfeng et al.

Source: IET Image Processing (Wiley-Blackwell); 10/16/2024, Vol. 18 Issue 12, p3460-3479, 20p

Subject Terms: TRANSFORMER models, IMAGE processing, FEATURE selection, DEEP learning, REMOTE sensing

Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments.

Authors: Sapkota, Ranjan Ahmed, Dawood Karkee, Manoj

Source: Artificial Intelligence in Agriculture; Sep2024, Vol. 13, p84-99, 16p

Subject Terms: ORCHARDS, AGRICULTURAL technology, IMAGE processing, AUTOMATION, MACHINE learning

Robust higher order potentials for enforcing label consistency

Authors: Kohli, P Ladický, L Torr, PHS

Relation: https://doi.org/10.1109/cvpr.2008.4587417

Availability: https://doi.org/10.1109/cvpr.2008.4587417
https://ora.ox.ac.uk/objects/uuid:74b37400-5337-4cca-a4e6-7b34a31de0c1

MSFA-Net: A Multiscale Feature Aggregation Network for Semantic Segmentation of Historical Building Point Clouds.

Authors: Zhang, Ruiju Xue, Yaqian Wang, Jian et al.

Source: Buildings (2075-5309); May2024, Vol. 14 Issue 5, p1285, 19p

Subject Terms: POINT cloud, PRESERVATION of architecture, HISTORIC preservation, HISTORIC buildings, DEEP learning

A Point Cloud Segmentation Method for Dim and Cluttered Underground Tunnel Scenes Based on the Segment Anything Model.

Authors: Kang, Jitong Chen, Ning Li, Mei et al.

Source: Remote Sensing; Jan2024, Vol. 16 Issue 1, p97, 23p

Subject Terms: TUNNELS, POINT cloud, DEEP learning, MACHINE learning, TUNNEL design & construction, TUNNEL lining

Optimal Pre-processing of Laser Scanning Data for Indoor Scene Analysis and 3D Reconstruction of Building Models.

Authors: Kim, Minju Kim, Harim

Source: KSCE Journal of Civil Engineering; Jan2024, Vol. 28 Issue 1, p1-14, 14p

From video to text: Semantic driving scene understanding using a coarse-to-fine method.

Authors: Fu, Huiyuan Ma, Huadong

Source: 2012 IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP); 1/ 1/2012, p1393-1396, 4p

Improved hierarchical conditional random field model for object segmentation.

Authors: Wang, Li-Li Yung, Nelson

Source: Machine Vision & Applications. Nov2015, Vol. 26 Issue 7/8, p1027-1043. 17p.

Subject Terms: *CONDITIONAL random fields, *IMAGE segmentation, *COST functions, *SUPERVISED learning, *BOUNDARY value problems

Robust higher order potentials for enforcing label consistency

Authors: Kohli, P Ladicky, L Torr, P H

Source: Robust higher order potentials for enforcing label consistency

Index Terms: journal article

URL: https://radar.brookes.ac.uk/radar/items/e70ce935-9726-a58f-650e-e4ffae4709d8/1/