Michalis Vrigkas, Ph.D.

I received my Ph.D. in 2016 from the Department of Computer Science & Engineering, University of Ioannina, Greece, under the supervision of Prof. Chistophoros Nikou. The topic of my Ph.D. dissertation was "Human Activity Recognition Using Conditional Random Fields and Privileged Information". I received the B.Sc. and M.Sc. in Computer Science from the University of Ioannina, Greece, in 2008 and 2010, respectively. Currently, I am a postdoctoral fellow with the University of Houston and member of the Computational Biomedicine Lab (CBL) and the Information Processing and Analysis research group (I.P.AN.). From 2015 to 2016, I was the Chair of the IEEE Student Branch of the University of Ioannina.

Currently, I am actively involved in learning efficient and discriminative image representations and provide solutions to challenging real-world problems. My research is mainly focused on Image and Video Processing. It covers a wide range of topics such as Computer Vision, Image Analysis, Machine Learning and Pattern Recognition. Special areas of research such as Medical Image Analysis, Biometrics, and Predictive Analytics have also attracted my interest. Each of the application areas described above employ a range of computer vision tasks; more or less well-defined measurement or processing problems, which can be solved using a variety of methods. I approach these problems with methods from signal processing and applied mathematics. You may find more details in the corrsponding publications section about my work.

Here you may find my curriculum vitae.

My publication list

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Journal Publications

  1.  M. Vrigkas, C. Nikou and I.A. Kakadiaris, “Identifying human behaviors using synchronized audio-visual cues,” IEEE Transactions on Affective Computing, vol. 8, no. 1, pp. 54-66, Jan.-March, 2017. [pdf] [original publication] [bibtex]
  2.  M. Vrigkas, C. Nikou and I.A. Kakadiaris, “A review of human activity recognition methods,” Frontiers in Robotics and Artificial Intelligence, vol. 2, no. 28, pp. 1-26, November 2015. [pdf] [original publication] [bibtex]
  3.  M. Vrigkas, C. Nikou and L.P. Kondi, “Robust maximum a posteriori image super-resolution,” Journal of Electronic Imaging, vol. 23, no. 4, pp. 043016, July 2014. [pdf] [original publication] [bibtex]
  4.  M. Vrigkas, V. Karavasilis, C. Nikou and I. A. Kakadiaris, “Matching mixtures of curves for human action recognition,” Computer Vision and Image Understanding, vol. 119, pp. 27-40, February 2014. [pdf] [original publication] [bibtex]
  5.  M. Vrigkas, C. Nikou and L.P. Kondi, “Accurate image registration for MAP image super-resolution,” Signal Processing: Image Communication, nol. 28, no. 5, pp. 494-508, May 2013. [pdf] [original publication] [bibtex]

Conference Publications

  1.  M. Vrigkas, E. Kazakos, C. Nikou and I.A. Kakadiaris, “Inferring human activities using robust privileged probabilistic learning,” in Proc. 4th Workshop on Transferring and Adapting Source Knowledge in Computer Vision in conjunction with the International Conference on Computer Vision (ICCV'17), Venice, Italy, October 22-29 2017. [pdf] [bibtex]
  2.  N. Sarafianos, M. Vrigkas and I.A. Kakadiaris, “Adaptive SVM+: Learning with privileged information for domain adaptation,” in Proc. in Proc. 4th Workshop on Transferring and Adapting Source Knowledge in Computer Vision in conjunction with the International Conference on Computer Vision (ICCV'17), Venice, Italy, October 22-29 2017. [pdf] [bibtex]
  3.  M. Vrigkas, C. Nikou and I.A. Kakadiaris, “Active privileged learning of human activities from weakly labeled samples,” in Proc. 23rd IEEE International Conference on Image Processing (ICIP'16), pp. 3036-3040, Phoenix, AZ, USA, September 25-28, 2016. [pdf] [bibtex]
  4.  M. Vrigkas, C. Nikou and I.A. Kakadiaris, “Exploiting privileged information for facial expression recognition,” in Proc. 9th IAPR/IEEE International Conference on Biometrics (ICB'16), pp. 1-8, Halmstad, Sweden, June 13-16, 2016. (Honorable Mention Paper Award) [pdf] [bibtex]
  5.  M.E. Plissiti, M. Vrigkas and C. Nikou, “Segmentation of cell clusters in Pap smear images using intensity variation between superpixels,” in Proc. International Conference on Systems, Signals and Image Processing (IWSSIP'15), pp. 184-187, London UK, September 10-12 2015. [pdf] [bibtex]
  6.  M. Vrigkas, C. Nikou and I.A. Kakadiaris, “Classifying behavioral attributes using conditional random fields,” in Proc. 8th Hellenic Conference on Artificial Intelligence (SETN'14), Lecture Notes in Computer Science, vol. 8445, pp. 95-104, Ioannina, Greece, May 15-17 2014. [pdf] [bibtex]
  7.  M. Vrigkas, V. Karavasilis, C. Nikou and I.A. Kakadiaris, “Action recognition by matching clustered trajectories of motion vectors,” in Proc. International Conference on Computer Vision Theory and Applications (VISSAP'13), pp. 112-117, Barcelona, Spain, February 21-24 2013. [pdf] [bibtex]
  8.  M. Vrigkas, C. Nikou and L.P. Kondi, “A fully robust framework for MAP image super-resolution,” in Proc. IEEE International Conference on Image Processing (ICIP'12), pp. 2225-2228, Orlando, FL, USA, September 30 - October 3 2012. [pdf] [bibtex]
  9.  M. Vrigkas, C. Nikou and L.P. Kondi, “On the improvement of image registration for high accuracy super-resolution,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'11), pp. 981-984, Prague, Czech Republic, May 22-27 2011. [pdf] [bibtex]

Download data/software

   The Parliament dataset is a collection of 228 video sequences, depicting political speeches in the Greek parliament, at a resolution of 320 × 240 pixels at 25 fps. All behaviors were recorded for 20 different subjects. The videos were acquired with a static camera and contain uncluttered backgrounds. The length of the video sequences is 250 frames. The video sequences were manually labeled with one of three behavioral labels: friendly (90 videos), aggressive (73 videos), or neutral (65 videos). The subjects express their opinion on a specific law proposal and they adjust their body movements and voice intensity level according to whether they agree with that or not.
   The dataset was annotated by two observers of Greek origin, who watched the videos independently and recorded their labels separately. Disagreement was resolved by a third observer. The observers were asked to categorize the videos with respect to the notions of kindness and aggressiveness according to a general perception of a political speech by a citizen with a Greek mentality as follows. (i) Subjects with large and abrupt body, head and hand movements and high speech signal amplitude are to be labeled as aggressive. This corresponds to statesmen who express strongly their disagreement with the topic discussed or a previous speech given by a political opponent. (ii) Subjects with very small variations in their motion and speech signal amplitude are to be labeled as neutral. This class includes standard political speeches only expressing a point of view without any strong indication (body motion or voice tone) of agreement or disagreement with the topic discussed. (iii) Subjects with large but smooth variations in the pose of their body and hands speaking with a normal speech signal amplitudes are to be labeled as friendly.
   If you use this dataset, I would be grateful if you cite with one of the following related publications:

Related Publications

  1.  M. Vrigkas, C. Nikou and I.A. Kakadiaris, “Identifying human behaviors using synchronized audio-visual cues,” IEEE Transactions on Affective Computing, vol. 8, no. 1, pp. 54-66, Jan.-March, 2017. [pdf] [original publication] [bibtex]
  2.  M. Vrigkas, C. Nikou and I.A. Kakadiaris, “Classifying behavioral attributes using conditional random fields,” in Proc. 8th Hellenic Conference on Artificial Intelligence, Lecture Notes in Computer Science, vol. 8445, pp. 95-104, Ioannina, Greece, May 15-17 2014. [pdf] [bibtex]

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Programming

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  • Programming in C - A.D. Marashall's book "Programming in C", online
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Contact Information

Feel free to communicate with me for any suggestions or comments about my work:


Michalis Vrigkas

Room B6, 2nd floor
University of Ioannina
Department of Computer Science & Engineering
P.O BOX 1186
45110 Ioannina
Greece
Phone: +30 265 100 8915
Web: www.cs.uoi.gr/~mvrigkas
Email: m v r i g k a s /at\ c s /dot/ u o i \dot/ g r (properly processed)