At Visage Technologies, we believe that continuous learning is the key to success. We love staying up-to-date with the latest trends through internal workshops, summer schools, industry-leading conferences, our own library… but also giving back to the community by organizing events such as our Computer Vision Talks conference.
We take pride in the fact that 70% of our team is fully dedicated to research and development. This page provides an overview of our team members’ scientific work that has been published so far.
Published papers
Bešenić, K., Ahlberg, J. & Pandžić, I.S. Picking out the bad apples: unsupervised biometric data filtering for refined age estimation. Vis Comput (2022).
Gogić, Ivan, Martina Manhart, Igor S. Pandžić, and Jörgen Ahlberg. Fast facial expression recognition using local binary features and shallow neural networks. The Visual Computer 36, no. 1 (2020): 97-112.
Markuš, Nenad, Ivan Gogić, Igor Pandžić, and Jörgen Ahlberg. Memory-efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment. In British Machine Vision Conference (BMVC), Northumbria University, Newcastle upon Tyne, UK, 3-6 September 2018, pp. 1-11. The British Machine Vision Association and Society for Pattern Recognition, 2018.
Gogić, Ivan, Jörgen Ahlberg, and Igor S. Pandžić. Regression-based methods for face alignment: A survey. Signal Processing (2020): 107755.
Dijana Vitas; Martina Tomic; Matko Burul. Traffic Light Detection in Autonomous Driving Systems. IEEE Consumer Electronics Magazine, Vol. 9, Iss. 4, pp. 90 – 96, 2020, DOI: 10.1109/mce.2020.2969156
Dijana Vitas; Martina Tomic; Matko Burul. Image Augmentation Techniques for Cascade Model Training. 2018 Zooming Innovation in Consumer Technologies Conference (ZINC), 30-31 May 2018, DOI: 10.1109/ZINC.2018.8448407
Bešenic, Krešimir, Jörgen Ahlberg, and Igor S. Pandzic. Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation. (2019).
N. Markuš, I. S. Pandžić, and J. Ahlberg. Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning from Unlabeled Videos and 3D-Shape Retrieval. IEEE Transactions on Image Processing, Vol. 28, Issue 1, pp. 279-290, January 2019.
N. Markuš, M. Frljak, I. S. Pandžić, J. Ahlberg, and R. Forchheimer. Eye pupil localization with an ensemble randomized trees. Pattern Recognition, Vol. 47, Issue 2, pp. 578-587, February 2014.
N. Markuš, I. S. Pandžić, and J. Ahlberg. Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion. International Conference on Pattern Recognition (ICPR), Cancun, Mexico, December 2016, pp. 2380-2385.
N. Markuš, M. Frljak, I. S. Pandžić, J. Ahlberg, and R. Forchheimer. High-performance face tracking. ACM 3rd International Symposium on Facial Analysis and Animation (FAA), Vienna, Austria, September 2012.
F. Dornaika and J. Ahlberg. Fitting 3D Face Models for Tracking and Active Appearance Model Training. Image and Vision Computing, Vol. 24, pp. 1010–1024, 2006.