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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F07%3A03134573%21RIV09-MSM-21230___
rdf:type
skos:Concept n17:Vysledek
dcterms:description
Which stereo algorithm is better? The one which is very dense but often erroneous or rather one which is very accurate but sparse? It depends on the application. In general, we can only say that the algorithm is better than the other if it is more accurate and denser. In literature, there exist several methods to evaluate quality of dense stereo matching algorithms. Their bottleneck is in tested algorithm parameter setting, which is assumed to be fixed. Such evaluation results are typically very different for various parameter setting in the sense they somehow change the tradeoff between accuracy and density. Therefore, we developed a new method for testing stereo algorithm based on the ROC-like analysis. We introduce ROC curves for stereo algorithms and define new numerical characteristics, which evaluate the algorithm itself, not a pair (algorithm, parameter setting) as it is in literature. Comparing ROC-curves of all tested algorithms we obtain the Feasibility Boundary. Which stereo algorithm is better? The one which is very dense but often erroneous or rather one which is very accurate but sparse? It depends on the application. In general, we can only say that the algorithm is better than the other if it is more accurate and denser. In literature, there exist several methods to evaluate quality of dense stereo matching algorithms. Their bottleneck is in tested algorithm parameter setting, which is assumed to be fixed. Such evaluation results are typically very different for various parameter setting in the sense they somehow change the tradeoff between accuracy and density. Therefore, we developed a new method for testing stereo algorithm based on the ROC-like analysis. We introduce ROC curves for stereo algorithms and define new numerical characteristics, which evaluate the algorithm itself, not a pair (algorithm, parameter setting) as it is in literature. Comparing ROC-curves of all tested algorithms we obtain the Feasibility Boundary. Which stereo algorithm is better? The one which is very dense but often erroneous or rather one which is very accurate but sparse? It depends on the application. In general, we can only say that the algorithm is better than the other if it is more accurate and denser. In literature, there exist several methods to evaluate quality of dense stereo matching algorithms. Their bottleneck is in tested algorithm parameter setting, which is assumed to be fixed. Such evaluation results are typically very different for various parameter setting in the sense they somehow change the tradeoff between accuracy and density. Therefore, we developed a new method for testing stereo algorithm based on the ROC-like analysis. We introduce ROC curves for stereo algorithms and define new numerical characteristics, which evaluate the algorithm itself, not a pair (algorithm, parameter setting) as it is in literature. Comparing ROC-curves of all tested algorithms we obtain the Feasibility Boundary.
dcterms:title
ROC Based Evaluation of Stereo Algorithms ROC Based Evaluation of Stereo Algorithms ROC Based Evaluation of Stereo Algorithms
skos:prefLabel
ROC Based Evaluation of Stereo Algorithms ROC Based Evaluation of Stereo Algorithms ROC Based Evaluation of Stereo Algorithms
skos:notation
RIV/68407700:21230/07:03134573!RIV09-MSM-21230___
n3:aktivita
n15:R n15:P
n3:aktivity
P(1ET101210406), R
n3:dodaniDat
n12:2009
n3:domaciTvurceVysledku
n4:9680411 n4:8930112 n4:7610203
n3:druhVysledku
n7:O
n3:duvernostUdaju
n10:S
n3:entitaPredkladatele
n11:predkladatel
n3:idSjednocenehoVysledku
448093
n3:idVysledku
RIV/68407700:21230/07:03134573
n3:jazykVysledku
n13:eng
n3:klicovaSlova
ROC analysis; computer vision; dense stereo; performance evaluation
n3:klicoveSlovo
n9:ROC%20analysis n9:computer%20vision n9:performance%20evaluation n9:dense%20stereo
n3:kontrolniKodProRIV
[D8D49BE3DF45]
n3:obor
n16:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n5:1ET101210406
n3:rokUplatneniVysledku
n12:2007
n3:tvurceVysledku
Čech, Jan Kostlivá, Jana Šára, Radim
n14:organizacniJednotka
21230