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researcher

Hao Wang

4 papers hereh-index 6246 citations17 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cond-mat.mtrl-sci1
  • eess.IV1
same name
  • Hao Wang — 79 papers, h 101
  • Hao Wang — 34 papers, h 18
  • Hao Wang — 33 papers, h 13
  • Hao Wang — 24 papers, h 7
  • Hao Wang — 23 papers, h 9
  • Hao Wang — 22 papers, h 48

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192024
most citedData augmentation in microscopic images for material data mining

126 citations · 129 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2024★ 3 cited

Enhancing Boundary Segmentation for Topological Accuracy with Skeleton-based Methods

Chuni Liu, Boyuan Ma, Xiaojuan Ban +5

Topological consistency plays a crucial role in the task of boundary segmentation for reticular images, such as cell membrane segmentation in neuron electron microscopic images, gr…

eess.IV2021

Data privacy protection in microscopic image analysis for material data mining

Boyuan Ma, Xiang Yin, Xiaojuan Ban +4

Recent progress in material data mining has been driven by high-capacity models trained on large datasets. However, collecting experimental data has been extremely costly owing to…

cs.CV2019

Boundary Learning by Using Weighted Propagation in Convolution Network

Wei Liu, Jiahao Chen, Chuni Liu +5

In material science, image segmentation is of great significance for quantitative analysis of microstructures. Here, we propose a novel Weighted Propagation Convolution Neural Netw…

cond-mat.mtrl-sci2019★ 126 cited

Data augmentation in microscopic images for material data mining

Boyuan Ma, Xiaoyan Wei, Chuni Liu +10

Recent progress in material data mining has been driven by high-capacity models trained on large datasets. However, collecting experimental data (real data) has been extremely cost…

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