M Mishra
Biographic Data
| ID | 42677 |
|---|---|
| NAME | M Mishra |
| GIVEN NAMES | M |
| FAMILY NAME | Mishra |
| SIGNATURE | MISHRA M |
| AFFILIATIONS | University of Minho |
| ORCID | 0000-0001-9321-5032 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 15 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Artificial intelligence-assisted visual inspection for cultural heritage
Applying computer science techniques such as artificial intelligence (AI), deep learning (DL), and computer vision (CV) on digital image data can help monitor and preserve cultural heritage (CH) sites. Defects such as weathering, removal of mortar, joint damage, discoloration, erosion, surface cracks, vegetation, seepage, and vandalism and their propagation with time adversely affect the structural health of CH sites. Several studies have reporte…
Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings
A prominent feature in Portuguese historic architecture is Portugal’s azulejos or tiles that cover cultural heritage buildings with colorful patterns. However, tiles are prone to deterioration due to the quality of masonry materials, exposure over time, and natural and human factors. A careful approach is necessary to detect and assess tile damage in time to conserve cultural heritage. Deep learning (DL) methods are applied to detect deterioratio…
Machine learning techniques for structural health monitoring of heritage buildings
Artificial intelligence-assisted visual inspection for cultural heritage
Applying computer science techniques such as artificial intelligence (AI), deep learning (DL), and computer vision (CV) on digital image data can help monitor and preserve cultural heritage (CH) sites. Defects such as weathering, removal of mortar, joint damage, discoloration, erosion, surface cracks, vegetation, seepage, and vandalism and their propagation with time adversely affect the structural health of CH sites. Several studies have reporte…
Machine learning techniques for structural health monitoring of heritage buildings
Artificial intelligence-assisted visual inspection for cultural heritage
Applying computer science techniques such as artificial intelligence (AI), deep learning (DL), and computer vision (CV) on digital image data can help monitor and preserve cultural heritage (CH) sites. Defects such as weathering, removal of mortar, joint damage, discoloration, erosion, surface cracks, vegetation, seepage, and vandalism and their propagation with time adversely affect the structural health of CH sites. Several studies have reporte…
Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings
A prominent feature in Portuguese historic architecture is Portugal’s azulejos or tiles that cover cultural heritage buildings with colorful patterns. However, tiles are prone to deterioration due to the quality of masonry materials, exposure over time, and natural and human factors. A careful approach is necessary to detect and assess tile damage in time to conserve cultural heritage. Deep learning (DL) methods are applied to detect deterioratio…
3D Surveying and Cultural Heritage (3 works) · Archaeology (3 works) · Construction engineering (3 works) · Cultural heritage (3 works) · Engineering (3 works) · Infrastructure Maintenance and Monitoring (3 works) · Architectural engineering (2 works) · Computer Science (2 works) · Forensic engineering (2 works) · Geography (2 works)