• 3 Citations
20162019
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Fingerprint Dive into the research topics where Amir Ghahremani is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Cameras Engineering & Materials Science
Bridge piers Engineering & Materials Science
Pixels Engineering & Materials Science
Water Engineering & Materials Science
Clustering algorithms Engineering & Materials Science
Ships Engineering & Materials Science
Detectors Engineering & Materials Science
Railroad cars Engineering & Materials Science

Research Output 2016 2019

  • 3 Citations
  • 5 Conference contribution
  • 3 Paper
  • 1 Poster

Cascaded CNN method for far object detection in outdoor surveillance

Ghahremani, A., Bondarev, E. & de With, P. H. N., 3 May 2019, Proceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018. Chbeir, R., di Baja, G. S., Gallo, L., Yetongnon, K., Dipanda, A. & Castrillon-Santana, M. (eds.). Piscataway: Institute of Electrical and Electronics Engineers, p. 40-47 8 p. 8706233

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
File
Processing
Ships
Cameras
Detectors
Object detection

Multi-class detection and orientation recognition of vessels in maritime surveillance

Ghahremani, A., Kong, Y., Bondarau, Y. & de With, P. H. N., 15 Jan 2019, (Accepted/In press)

Research output: Contribution to conferencePaperAcademic

Labels

Re-identification of vessels with convolutional neural networks

Ghahremani, A., Kong, Y., Bondarev, E. & de With, P. H. N., 1 Jan 2019, p. 93-97 5 p.

Research output: Contribution to conferencePaperAcademic

Neural networks
Cameras
1 Citation (Scopus)

Self-learning framework with temporal filtering for robust maritime vessel detection

Ghahremani, A., Bondarev, E. & de With, P. H. N., 5 May 2019, Representations, Analysis and Recognition of Shape and Motion from Imaging Data - 7th International Workshop, RFMI 2017, Revised Selected Papers. Ghorbel, F., Chen, L. & Ben Amor, B. (eds.). Cham: Springer, p. 121-135 15 p. (Communications in Computer and Information Science; vol. 842)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Self-learning
Vessel
Filtering
Detectors
Detector

Towards multi-class detection: A self-learning approach to reduce inter-class noise from training dataset

Ghahremani, A., Bondarev, E. & De With, P. H. N., 1 Jan 2019, Eleventh International Conference on Machine Vision, ICMV 2018. Verikas, A., Nikolaev, D. P., Radeva, P. & Zhou, J. (eds.). SPIE, 8 p. 110411M. (Proceedings of SPIE; vol. 11041)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Self-learning
Multi-class
learning
education
Object Model