The design of AVS has its roots in the modelling of the human visual system (HVS); an extremely challenging task that generations of researchers have attempted with limited success.

Vision is a very natural capability and it is commonly accepted that about 80% of what we perceive is vision-based. Vision’s highly intuitive nature makes it difficult for us to understand the myriad of problems associated with designing AVS, in contrast to sophisticated analytic tasks such as playing chess.Foto: Kristoffer Öfjäll

Thus AVS became a widely underestimated scientific problem, maybe one of the most underestimated problems of the past decades.

Many AI researchers believed that the real challenges were symbolic and analytic problems and visual perception was just a simple sub-problem, to be dealt with in a summer project, which obviously failed.

The truth is that computers are better than humans at playing chess, but even a small child has better generic vision capabilities than any artificial system.

My research aims at improving AVS capabilities substantially, driven by an HVS-inspired approach, as AVS are supposed to coexist with – and therefore predict actions of – humans.

 

Selected Publications
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Bertil Grelsson, Michael Felsberg, Folke Isaksson (2016)

Journal of Field Robotics , Vol.33 , s.967-993 Continue to DOI

Michael Felsberg, Kristoffer Öfjäll, Reiner Lenz (2015)

, Frontiers in Robotics and AI , Vol.2 Continue to DOI

Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg (2015)

Proceedings of the International Conference in Computer Vision (ICCV), 2015 , s.4310-4318 Continue to DOI

More Publications
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2019

Matej Kristan, Aleš Leonardis, Jirí Matas, Michael Felsberg, Roman Pflugfelder, Luka Cehovin Zajc, Tomáš Vojírì, Goutam Bhat, Alan Lukezič, Abdelrahman Eldesokey, Gustavo Fernández, Álvaro García-Martín, Álvaro Iglesias-Arias, A. Aydin Alatan, Abel González-García, Alfredo Petrosino, Alireza Memarmoghadam, Andrea Vedaldi, Andrej Muhič, Anfeng He, Arnold Smeulders, Asanka G. Perera, Bo Li, Boyu Chen, Changick Kim, Changsheng Xu, Changzhen Xiong, Cheng Tian, Chong Luo, Chong Sun, Cong Hao, Daijin Kim, Deepak Mishra, Deming Chen, Dong Wang, Dongyoon Wee, Efstratios Gavves, Erhan Gundogdu, Erik Velasco-Salido, Fahad Shahbaz Khan, Fan Yang, Fei Zhao, Feng Li, Francesco Battistone, George De Ath, Gorthi R. K. S. Subrahmanyam, Guilherme Bastos, Haibin Ling, Hamed Kiani Galoogahi, Hankyeol Lee, Haojie Li, Haojie Zhao, Heng Fan, Honggang Zhang, Horst Possegger, Houqiang Li, Huchuan Lu, Hui Zhi, Huiyun Li, Hyemin Lee, Hyung Jin Chang, Isabela Drummond, Jack Valmadre, Jaime Spencer Martin, Javaan Chahl, Jin Young Choi, Jing Li, Jinqiao Wang, Jinqing Qi, Jinyoung Sung, Joakim Johnander, Joao Henriques, Jongwon Choi, Joost van de Weijer, Jorge Rodríguez Herranz, José M. Martínez, Josef Kittler, Junfei Zhuang, Junyu Gao, Klemen Grm, Lichao Zhang, Lijun Wang, Lingxiao Yang, Litu Rout, Liu Si, Luca Bertinetto, Lutao Chu, Manqiang Che, Mario Edoardo Maresca, Martin Danelljan, Ming-Hsuan Yang, Mohamed Abdelpakey, Mohamed Shehata, Myunggu Kang, Namhoon Lee, Ning Wang, Ondrej Miksik, P. Moallem, Pablo Vicente-Moñivar, Pedro Senna, Peixia Li, Philip Torr, Priya Mariam Raju, Qian Ruihe, Qiang Wang, Qin Zhou, Qing Guo, Rafael Martín-Nieto, Rama Krishna Gorthi, Ran Tao, Richard Bowden, Richard Everson, Runling Wang, Sangdoo Yun, Seokeon Choi, Sergio Vivas, Shuai Bai, Shuangping Huang, Sihang Wu, Simon Hadfield, Siwen Wang, Stuart Golodetz, Tang Ming, Tianyang Xu, Tianzhu Zhang, Tobias Fischer, Vincenzo Santopietro, Vitomir Štruc, Wang Wei, Wangmeng Zuo, Wei Feng, Wei Wu, Wei Zou, Weiming Hu, Wengang Zhou, Wenjun Zeng, Xiaofan Zhang, Xiaohe Wu, Xiao-Jun Wu, Xinmei Tian, Yan Li, Yan Lu, Yee Wei Law, Yi Wu, Yiannis Demiris, Yicai Yang, Yifan Jiao, Yuhong Li, Yunhua Zhang, Yuxuan Sun, Zheng Zhang, Zheng Zhu, Zhen-Hua Feng, Zhihui Wang, Zhiqun He (2019) The Sixth Visual Object Tracking VOT2018 Challenge Results Computer Vision – ECCV 2018 Workshops , s. 3-53 Continue to DOI

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