Dr. Tomasz Trzcinski

Chief Scientist
Tooploox, Poland


Highest Degree
Ph.D. in Computer Vision from Ecole Polytechnique Federale De Lausanne, Switzerland

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Biography

Dr. Tomasz Trzcinski is currently working as Chief Scientist at Tooploox, Wroclaw/ Warsaw, Poland, and Associate Professor at Warsaw University of Technology, Warsaw, Poland. He has completed his Ph.D. in Computer Vision from Ecole Polytechnique Federale De Lausanne, Switzerland. His previous work includes worked as Research Assistant in Computer Vision Lab at Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland, and Software Engineer Intern in Knowledge Search Infrastructure Team at Google, Zurich, Switzerland, and System Engineering Trainee in Corporate Research & Development at Qualcomm, San Diego, CA, USA, and Research Assistant in Augmented Reality Group at Telefonica R&D, Barcelona, Spain. He is also serving as PhD Student Mentor in the EPFL Graduate Student Association, and committee member of the EPFL Consulting Society. He was received Departmental fellowship of the School of Computer and Communication Sciences at EPFL, Nokia scholarship for an exceptional performance during the DTU Summer University, and Academic scholarships of Poznan University of Technology. He is author and co-author of 8 journal papers.

Area of Interest:

Computer Sciences
Computer Vision
Pattern Recognition
Pattern Analysis
Machine Intelligence

Selected Publications

  1. Trzcinski, T., M. Christoudias and V. Lepetit, 2015. Learning image descriptors with boosting. IEEE Trans. Pattern Anal. Mach. Intelli., 37: 597-610.
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  2. Fan, B., Q. Kong, T. Trzcinski, Z. Wang, C. Pan and P. Fua, 2014. Receptive fields selection for binary feature description. IEEE Trans. Image Proces., 23: 2583-2595.
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  3. Trzcinski, T., V. Lepetit and P. Fua, 2012. Thick boundaries in binary space and their influence on nearest-neighbor search. Pattern Recognit. Lett., 33: 2173-2180.
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  4. Calonder, M., V. Lepetit, M. Ozuysal, T. Trzcinski, C. Strecha and P. Fua, 2012. BRIEF: Computing a local binary descriptor very fast. IEEE Trans. Pattern Anal. Mach. Intelli., 34: 1281-1298.
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