Robust Computer Vision: An Interdisciplinary Challenge

Peter Meer
Electrical and Computer Engineering Department
Rutgers University
94 Brett Road
Piscataway, NJ 08854­8058
E­mail: meer@caip.rutgers.edu


Charles V. Stewart
Computer Science Department
Rensselaer Polytechnic Institute
110 8th Street
Troy, NY 12180­3590
E­mail: stewart@cs.rpi.edu


David E. Tyler
Statistics Department
Rutgers University
110 Frelinghuysen Road
Piscataway, NJ 08854­8018
E­mail: dtyler@caip.rutgers.edu




This special issue is dedicated to examining the use of techniques from robust statistics in solving computer vision problems. It represents a milestone of recent progress within a subarea of our field that is nearly as old as the field itself, but has seen rapid growth over the past decade. Our guest editorial considers the meaning of robustness in computer vision, summarizes the papers, and outlines the relationship between techniques in computer vision and statistics as a means of highlighting future directions. It complements the available reviews on this topics [12, 13].

Computer Vision and Image Understanding , 78, Vol. 1--7, 2000.


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