• 1487 Citations
  • 19 h-Index
1984 …2019
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Fingerprint Dive into the research topics where T. Szirányi is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Cameras Engineering & Materials Science
Cellular neural networks Engineering & Materials Science
Textures Engineering & Materials Science
Pixels Engineering & Materials Science
Color Engineering & Materials Science
Remote sensing Engineering & Materials Science
Statistics Engineering & Materials Science
Processing Engineering & Materials Science

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

Research Output 1984 2019

  • 1487 Citations
  • 19 h-Index
  • 103 Conference contribution
  • 56 Article
  • 5 Chapter
  • 1 Conference article

A hybrid CNN approach for single image depth estimation: A case study

Harsányi, K., Kiss, A., Majdik, A. & Szirányi, T., Jan 1 2019, Multimedia and Network Information Systems - Proceedings of the 11th International Conference MISSI 2018. Choros, K., Kopel, M., Kukla, E. & Sieminski, A. (eds.). Springer Verlag, p. 372-381 10 p. (Advances in Intelligent Systems and Computing; vol. 833).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Decoding
Robotics
Cameras
Calibration
Neural networks

No-reference video quality assessment via pretrained CNN and LSTM networks

Varga, D. & Szirányi, T., Jan 1 2019, In : Signal, Image and Video Processing.

Research output: Contribution to journalArticle

Open Access
Neural networks
Time series
Long short-term memory
Experiments

Street object classification via LIDARs with only a single or a few layers

Rozsa, Z. & Szirányi, T., May 6 2019, IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018. Institute of Electrical and Electronics Engineers Inc., p. 156-161 6 p. 8708881. (IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Intelligent vehicle highway systems
Sensors
Object detection
Deep learning