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Imaging / Optics / Object recognition / 3D single object recognition / Scale-invariant feature transform / Scale space / Template matching / Segmentation / SURF / Computer vision / Image processing / Vision


Document Date: 1999-06-28 22:51:41

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City

Cambridge / /

Company

Oxford University Press / /

Country

Puerto Rico / United Kingdom / /

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Facility

Local Scale-Invariant Features David G. Lowe Computer Science Department University of British Columbia Vancouver / /

IndustryTerm

search method / probabilistic best-bin-first search / computer vision systems / low-residual least-squares solution / approximate nearest-neighbour search / least-square solution / least-squares solution / k-d tree algorithm / affine parameter solution / affine solution / parameter solution / final least-squares solution / structure-from-motion solutions / basic tool / using approximate nearest-neighbour search / projected using the affine parameter solution / /

Organization

Machine Intelligence / Local Scale-Invariant Features David G. Lowe Computer Science Department University / Oxford University / University of British Columbia / Pattern Analysis and Machine Intelligence / /

Person

Andrea Selinger / Nathan Intrator / Alice C. Parker / Keiji Tanaka / Mike W. Oram / James L. Crowley / David G. Lowe / Hiroshi Tamura / Ai / Jon Pauls / Nancy G. Kanwisher / Ichiro Fujita / Tomaso Poggio / Ronen / Jeff / Shree K. Nayar / Shimon / /

PublishedMedium

Current Opinion / /

Technology

neuroscience / machine vision / 3-D / 10 processor / k-d tree algorithm / /

URL

http /

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