C
Christopher Rasmussen
Researcher at University of Delaware
Publications - 56
Citations - 2028
Christopher Rasmussen is an academic researcher from University of Delaware. The author has contributed to research in topics: Inpainting & Image segmentation. The author has an hindex of 23, co-authored 55 publications receiving 1953 citations. Previous affiliations of Christopher Rasmussen include University UCINF & National Institute of Standards and Technology.
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Journal ArticleDOI
Probabilistic data association methods for tracking complex visual objects
TL;DR: A randomized tracking algorithm adapted from an existing probabilistic data association filter (PDAF) that is resistant to clutter and follows agile motion is introduced and a related technique that allows mixed tracker modalities and handles object overlaps robustly is derived.
Proceedings ArticleDOI
Grouping dominant orientations for ill-structured road following
TL;DR: An algorithm for following ill-structured roads in which dominant texture orientations computed with multi-scale Gabor wavelet filters vote for a consensus road vanishing point location is described.
Proceedings ArticleDOI
Combining laser range, color, and texture cues for autonomous road following
TL;DR: Results on combining depth information from a laser range-finder and color and texture image cues to segment ill-structured dirt, gravel, and asphalt roads as input to an autonomous road following system are described.
Journal ArticleDOI
Alice: An information‐rich autonomous vehicle for high‐speed desert navigation
Lars B. Cremean,Tully Foote,Jeremy H. Gillula,George H. Hines,Dmitriy Kogan,Kristopher L. Kriechbaum,Jeffrey C. Lamb,Jeremy Leibs,Laura Lindzey,Christopher Rasmussen,Alexander D. Stewart,Joel W. Burdick,Richard M. Murray +12 more
TL;DR: This paper describes the implementation and testing of Alice, the California Institute of Technology’s entry in the 2005 DARPA Grand Challenge, which encountered a combination of sensing and control issues in the Grand Challenge Event that led to a critical failure after traversing approximately 8 miles.
Proceedings ArticleDOI
Joint probabilistic techniques for tracking multi-part objects
TL;DR: A framework for combining and sharing information among several state estimation processes operating on the same underlying visual object is presented, and a measure of tracker confidence is formulated, based on distinctiveness and occlusion probability, which permits the deactivation of trackers before erroneous state estimates adversely affect the ensemble.