Ivan V. Bajić
School of Engineering Science
Simon Fraser University
8888 University Drive
Burnaby, BC, V5A 1S6, Canada
I am a Professor of Engineering Science at Simon Fraser University. My professional interests revolve around signal processing, machine learning, and their applications in image and video processing, coding, communications, and multimedia ergonomics. In addition to research, teaching, and consulting in these areas, I was also involved in new media art as a Telepresence Architect for several telematic dance/music performances in 2009-2010.
I was born in Belgrade, Serbia, in 1976. I received the B.Sc.Eng. degree (summa cum laude) in Electronic Engineering from the University of Natal, South Africa, in 1998, and the M.S. degree in Electrical Engineering, the M.S. degree in Mathematics, and the Ph.D. degree in Electrical Engineering from Rensselaer Polytechnic Institute, Troy, NY, USA, in 2000, 2002, and 2003, respectively.
Besides professional life, I am also a wine enthusiast. I’ve been fortunate enough to visit some of the top wine producing regions in the world in the last number of years, including the Napa and Sonoma valleys in California, Cape Town/Stellenbosch region in South Africa, La Rioja in Spain, Douro valley in Portugal, Chianti in Italy and, closer to home, Okanagan and Cowichan valleys in British Columbia, as well as the Olympic Peninsula wineries in Washington State.
At SFU (2005 - present):
At UM (2003-2005):
My research interests are in the field of signal processing and its many applications. Signal processing is the science behind our digital life. In our work, my students, collaborators, and I, use the tools from signal processing, machine/deep learning, probability, statistics and optimization to analyze and solve problems related to all kinds of signals - images, video, audio, power - and others. Most of this work takes place at the Multimedia Lab and the Computational Sustainability Lab. Some of my ongoing application projects are listed below; please click on the corresponding image to learn more.
The word ergonomics probably makes you think about comfortable chairs or pillows. But physical objects are not the only things we interact with. You likely spend a good portion of each day looking at images or video, listening to music, or browsing websites. Are these digital objects comfortable? We want to understand how people interact with digital objects, especially multimedia signals, in order to facilitate better user experience and more seamless interaction with our digital environment. Read more
Remember the movie The Matrix, where software agents and trained human operators are able to spot objects and people from endless streams of digital data? Is this really possible? For humans, unlikely. But we have suceeded in training machines to do something like that. Being able to analyze compressed streams, without decoding, is one of the key technologies needed to handle massive amounts of video in the era of Big Data. Read more
Point clouds are sets of points in 3D space that describe the surface or shape of an object, and may carry additional attributes such as color. They were used for some time in computer graphics and animation industry, then 3D printing, and are now making their way towards mainstream through virtual/augmented reality and immersive media. Their irregular sampling makes their processing more challenging. Read more
Sound fields are acoustic counterparts of 3D video. They are essential for a covincing sense of immersion in virtual and augmented reality. Our work in this area is on spatial sound field control in noisy environments, array element placement, array signal processing, as well as study and design of loudspeaker and microphone array patterns. Read more
Computational sustainability tries to balance the needs of the environment, the economy, and society to solve sustainability problems using computational tools, models and algorithms. We are particularly interested in solving climate change and energy challenges through the theme of conservation and habitual change. To this end, we work with both academic and industry partners to develop and commercialize our discoveries and solutions. Read more
Current group members:
Former group members:
The software below is provided as is, without any warranty, expressed or implied. It is free for academic and non-commercial use. If you use the software in your research, please cite the corresponding references.Adaptive hole filling for 3D point clouds - Implementation of exemplar-based hole filling from the SPL 2018 paper. Download.
The datasets below are provided without any warranty, expressed or implied. They are free for academic and non-commercial use. If you use the data in your research, please cite the corresponding references.
Eye tracking database for standard video sequences - This dataset includes a database of gaze locations by 15 independent viewers on a set of 12 standard CIF video sequences: Foreman, Bus, City, Crew, Flower Garden, Mother and Daughter, Soccer, Stefan, Mobile Calendar, Harbor, and Tempete. Included are the gaze locations for the first and second viewing of each sequence, their visualizations, heat maps, and sample MATLAB demo files that show how to use the data.
Segmented foreground objects - This dataset includes manually segmented foreground objects that we used as the ground truth in our moving region segmentation. Each set contains segmentation masks, segmented object(s), and original frames.
No openings at the moment