Nebojsa Jojic
Microsoft Corp., One Microsoft Way, Redmond, WA 98052                                              
425-705-5865                
     
e-mail: jojic@microsoft.com
URL:    http://www.research.microsoft.com/~jojic,  Very old: http://www.ifp.uiuc.edu/~jojic

Under construction: I am reviving my web page. For now, here are some newer links:

All-I-Have-Seen dataset and stel epitome

A paper on coevolution of a family of human immune system molecules (MHC class I) and viruses

More to come... In the mean time, some older stuff:  Epitomic Analysis of Appearance and Shape, ICCV 2003

                                                        

                              An audio-visual graphical model, ICASSP 2002

                                 

                                A generative model for layered vision, CVPR 2001     

I am interested in machine learning approaches to the automatic extraction of medium level representation of natural signals. The hope is that by structuring statistical generative models to mimic the structure of the real world, the models will be able to automatically adapt to audio, visual or multimodal signals during the unsupervised model fitting (learning) stage, thus providing a medium-level representation suitable for compression, transmission, search, editing, enhanced viewing experience, etc. These models are object-based, where an object can produce sounds, have a changing appearance, move and be exposed to attenuation in audio domain, illumination in video domain, and, when other objects are present, to occlusion or additive mixing in both domains. The main requirement is complete adaptivity, i.e., avoiding any design parameters preset for a very specialized application. For example, the same model should be applicable to tracking a person in front of a cluttered background, and to tracking a flock of birds. The tracking task, as well as many other tasks performed jointly,  such as de-noising, dynamic mosaic building or object removal as well as separating audio sources and associating them to object appearances, are all achievable as probabilistic queries, i.e., inference of the hidden variables associated to the world structure. All this should be doable using the data itself, without special application-specific initialization procedure or the separate supervised training stage.


QUICK REFERENCE


EDUCATION
University of Illinois, Urbana-Champaign, IL
Ph.D. , Electrical and Computer Engineering, 2001.
thesis title: "Generative models for computer vision"
M.S., Electrical and Computer Engineering, October 1997.
thesis title: "Computer Modeling, Analysis and Synthesis of Dressed Humans"
Advisor: Prof. Thomas S. Huang

University of Belgrade, Belgrade, Yugoslavia
B.S., Electrical Engineering, June, 1995
B.S. thesis title: "Image compression using wavelets and scalar coding of sub-bands", advisor: Prof. Miodrag Popovic


PUBLICATIONS

Book Chapters:

Journals: M.S. Thesis: "Computer Modeling, Analysis and Synthesis of Dressed Humans"

Ph.D. Thesis: "Generative Models for Computer Vision"

Conferences:

Acknowledgements: Most of the research was funded by Microsoft, ARL under the cooperative agreement No. DAAL01-96-2-0003  and NSF  (grant  No. IRI-9634618). The work I did in Hong Kong was also funded by the Hong Kong Industry and Technology Development Council (grant No. AF/122/96).


PATENTS

HONORS
 
Last update:  January 2002.