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Personal profile

Personal profile

Dr. Michael Ying Yang is currently Assistant Professor with University of Twente (the Netherlands), heading a group working on scene understanding. 

He received the PhD degree (summa cum laude) from University of Bonn (Germany) in 2011. From 2008 to 2012, he worked as Researcher with the Department of Photogrammetry, University of Bonn. From 2012 to 2015, he was a Postdoctoral Researcher with the Institute for Information Processing, Leibniz University Hannover. From 2015 to 2016, he was a Senior Researcher at TU Dresden.

His research interests are in the fields of computer vision and photogrammetry with specialization on scene understanding and semantic interpretation from imagery. He published over 80 articles in international journals and conference proceedings and currently co-supervise 4 PhD students. He serves as Associate Editor of ASPRS Photogrammetric Engineering & Remote Sensing, co-chair of ISPRS working group II/5 Dynamic Scene Analysis, and recipient of the ISPRS President's Honorary Citation (2016) and Best Science Paper Award at BMVC 2016. He is guest editor of 4 journal special issues. Since 2016, he is a Senior Member of IEEE. He is regularly serving as program committee member of conferences and reviewer for international journals.

Teaching

Activities in education

Main educational responsibilities are teaching topics on image processing, 3D modeling and photogrammetry.

Research interests

Activities in research

His research is in the fields of Computer Vision and Photogrammetry with specialization on Deep Learning, Graphical Models, Scene Understanding, and Multi-Sensor Fusion.

Current supervision (co-promoter) of PhD students in ITC:

Zhenchao Zhang (funded by CSC)

Sophie Crommelinck (funded by EU H2020)

Ye Lv (funded by CSC)

Keywords

  • QA75 Electronic computers. Computer science
  • computer vision
  • photogrammetry
  • image processing
  • Artificial intelligence
  • machine learning

Fingerprint Dive into the research topics where Michael Yang is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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Image classification Engineering & Materials Science
Semantics Engineering & Materials Science
segmentation Earth & Environmental Sciences
Computer vision Engineering & Materials Science
Remote sensing Engineering & Materials Science
Image matching Engineering & Materials Science
image classification Earth & Environmental Sciences
Photogrammetry Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2009 2019

Accurate salient object detection via dense recurrent connections and residual-based hierarchical feature integration

Cao, Y., Fu, G., Yang, J., Cao, Y. & Yang, M. Y., 1 Oct 2019, In : Signal Processing: Image Communication. 78, p. 103-112 10 p.

Research output: Contribution to journalArticleAcademicpeer-review

Neural networks
Image reconstruction
Computer vision
Processing
Object detection

A deep-learning-based approach for fast and robust steel surface defects classification

Fu, G., Sun, P., Zhu, W., Yang, J., Cao, Y., Yang, M. Y. & Cao, Y., 1 Oct 2019, In : Optics and Lasers in Engineering. 121, p. 397-405 9 p.

Research output: Contribution to journalArticleAcademicpeer-review

Steel
Surface defects
surface defects
learning
steels
1 Downloads (Pure)

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

Crommelinck, S., Koeva, M. N., Yang, M. Y. & Vosselman, G., 25 Oct 2019, In : Remote sensing. 11, 21, p. 1-22 22 p., 2505.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
imagery
learning
remote sensing
segmentation
surveying
35 Downloads (Pure)

Automating image-based cadastral boundary mapping

Crommelinck, S. C., 25 Oct 2019, Enschede: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). 189 p.

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

Open Access
File
1 Citation (Scopus)

Box-level segmentation supervised deep neural networks for accurate and real-time multispectral pedestrian detection

Cao, Y., Guan, D., Wu, Y., Yang, J., Cao, Y. & Yang, M. Y., Apr 2019, In : ISPRS journal of photogrammetry and remote sensing. 150, April, p. 70-79 10 p.

Research output: Contribution to journalArticleAcademicpeer-review

pedestrian
segmentation
boxes
Infrared radiation
Anchors

Prizes

Senior Member of IEEE

Michael Yang (Recipient), 2016

Prize: Honorary award