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  • Není k dispozici (cs)
  • Realistic approaches to large scale object recognition, i.e. for detection and localisation of hundreds or more objects, must support sub-linear time indexing. In the paper, we propose a method capable of recognising one of N objects in log(N) time. The .visual memory. is organised as a binary decision tree that is built to minimise average time to decision. Leaves of the tree represent a few local image areas, and each non-terminal node is associated with a .weak classifier.. In the recognition phase, a single invariant measurement decides in which subtree a corresponding image area is sought. The method preserves all the strengths of local affine region methods . robustness to background clutter, occlusion, and large changes of viewpoints. Experimentally we show that it supports near real-time recognition of hundreds of objects with state-of-the-art recognition rates. After the test image is processed (in a second on a current PCs), the recognition via indexing into the visual memory
  • Realistic approaches to large scale object recognition, i.e. for detection and localisation of hundreds or more objects, must support sub-linear time indexing. In the paper, we propose a method capable of recognising one of N objects in log(N) time. The .visual memory. is organised as a binary decision tree that is built to minimise average time to decision. Leaves of the tree represent a few local image areas, and each non-terminal node is associated with a .weak classifier.. In the recognition phase, a single invariant measurement decides in which subtree a corresponding image area is sought. The method preserves all the strengths of local affine region methods . robustness to background clutter, occlusion, and large changes of viewpoints. Experimentally we show that it supports near real-time recognition of hundreds of objects with state-of-the-art recognition rates. After the test image is processed (in a second on a current PCs), the recognition via indexing into the visual memory (en)
Title
  • Sub-linear Indexing for Large Scale Object Recognition
  • Není k dispozici (cs)
  • Sub-linear Indexing for Large Scale Object Recognition (en)
skos:prefLabel
  • Sub-linear Indexing for Large Scale Object Recognition
  • Není k dispozici (cs)
  • Sub-linear Indexing for Large Scale Object Recognition (en)
skos:notation
  • RIV/68407700:21230/05:03114548!RIV06-GA0-21230___
http://linked.open.../vavai/riv/strany
  • 1 ; 10
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/03/0440)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 545351
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/05:03114548
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • LAF; MSER; Object recognition; local affine frames (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [3BCB9CF2FD85]
http://linked.open...v/mistoKonaniAkce
  • Oxford
http://linked.open...i/riv/mistoVydani
  • London
http://linked.open...i/riv/nazevZdroje
  • BMVC 2005: Proceedings of the 16th British Machine Vision Conference
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Matas, Jiří
  • Obdržálek, Štěpán
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • British Machine Vision Association
https://schema.org/isbn
  • 1-901725-29-4
http://localhost/t...ganizacniJednotka
  • 21230
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