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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F13%3A00212519%21RIV14-GA0-21230___
rdf:type
n6:Vysledek skos:Concept
dcterms: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.
dcterms:title
Star Convex Object Detection by the Infinite Shape Mixture Model Star Convex Object Detection by the Infinite Shape Mixture Model
skos:prefLabel
Star Convex Object Detection by the Infinite Shape Mixture Model Star Convex Object Detection by the Infinite Shape Mixture Model
skos:notation
RIV/68407700:21230/13:00212519!RIV14-GA0-21230___
n6:predkladatel
n7:orjk%3A21230
n3:aktivita
n20:P
n3:aktivity
P(GAP202/12/2071)
n3:dodaniDat
n10:2014
n3:domaciTvurceVysledku
n8:2572265
n3:druhVysledku
n19:D
n3:duvernostUdaju
n5:S
n3:entitaPredkladatele
n13:predkladatel
n3:idSjednocenehoVysledku
107652
n3:idVysledku
RIV/68407700:21230/13:00212519
n3:jazykVysledku
n16:eng
n3:klicovaSlova
Object detection; Bayesian inference
n3:klicoveSlovo
n15:Object%20detection n15:Bayesian%20inference
n3:kontrolniKodProRIV
[232C1F570801]
n3:mistoKonaniAkce
Hernstein
n3:mistoVydani
Vienna
n3:nazevZdroje
CVWW 2013: Proceedings of the 18th Computer Vision Winter Workshop
n3:obor
n4:JD
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n9:GAP202%2F12%2F2071
n3:rokUplatneniVysledku
n10:2013
n3:tvurceVysledku
Sixta, Tomáš
n3:typAkce
n12:WRD
n3:zahajeniAkce
2013-02-04+01:00
s:numberOfPages
7
n22:hasPublisher
Vienna University of Technology
n17:isbn
978-3-200-02943-9
n21:organizacniJednotka
21230