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Description
  • Shape is an important feature of many object categories. In this paper we propose a Bayesian framework for detection of unknown number of objects based on their shape. The task is formulated as a minimization of Bayesian risk. The loss function is designed in such a way that the number of objects need not to be known or even bounded. We introduce a probability distribution over object states (number of objects and their poses) called Infinite Shape Mixture Model which is a modification of Rasmussen's Infinite Gaussian Mixture Model. Conditional posterior distributions are derived for all parameters of the model in order to make the inference feasible. Performance of the model is tested on two brief experiments.
  • Shape is an important feature of many object categories. In this paper we propose a Bayesian framework for detection of unknown number of objects based on their shape. The task is formulated as a minimization of Bayesian risk. The loss function is designed in such a way that the number of objects need not to be known or even bounded. We introduce a probability distribution over object states (number of objects and their poses) called Infinite Shape Mixture Model which is a modification of Rasmussen's Infinite Gaussian Mixture Model. Conditional posterior distributions are derived for all parameters of the model in order to make the inference feasible. Performance of the model is tested on two brief experiments. (en)
Title
  • Star Convex Object Detection by the Infinite Shape Mixture Model
  • Star Convex Object Detection by the Infinite Shape Mixture Model (en)
skos:prefLabel
  • Star Convex Object Detection by the Infinite Shape Mixture Model
  • Star Convex Object Detection by the Infinite Shape Mixture Model (en)
skos:notation
  • RIV/68407700:21230/13:00212519!RIV14-GA0-21230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GAP202/12/2071)
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
  • 107652
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/13:00212519
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Object detection; Bayesian inference (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [232C1F570801]
http://linked.open...v/mistoKonaniAkce
  • Hernstein
http://linked.open...i/riv/mistoVydani
  • Vienna
http://linked.open...i/riv/nazevZdroje
  • CVWW 2013: Proceedings of the 18th Computer Vision Winter Workshop
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
  • Sixta, Tomáš
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • Vienna University of Technology
https://schema.org/isbn
  • 978-3-200-02943-9
http://localhost/t...ganizacniJednotka
  • 21230
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