Integrated Detection Network for Multiple Object Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00237809" target="_blank" >RIV/68407700:21230/15:00237809 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/B9780128025819000068" target="_blank" >http://www.sciencedirect.com/science/article/pii/B9780128025819000068</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/B978-0-12-802581-9.00006-8" target="_blank" >10.1016/B978-0-12-802581-9.00006-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Integrated Detection Network for Multiple Object Recognition

Popis výsledku v původním jazyce

Recognizing multiple objects involves two interdependent tasks, object localization and classification. The goal of the object localization is to accurately find the object pose parameters relative to an established reference, such as the origin of the image coordinate system. The object classification assigns class labels to the objects according to the prespecified categories. Multiobject recognition has been previously solved by designing a set of individual single-object detectors or by training a combined multiobject detection and classification system. In the medical domain, these models can be further improved by relying on strong spatial prior information present in medical images of a human body. This chapter describes how the spatial prior can be used to recognize multiple anatomical structures, which results in the integrated detection network. The structures are recognized sequentially, one by one, using optimal order such that the later recognitions can benefit from constr

Název v anglickém jazyce

Integrated Detection Network for Multiple Object Recognition

Popis výsledku anglicky

Recognizing multiple objects involves two interdependent tasks, object localization and classification. The goal of the object localization is to accurately find the object pose parameters relative to an established reference, such as the origin of the image coordinate system. The object classification assigns class labels to the objects according to the prespecified categories. Multiobject recognition has been previously solved by designing a set of individual single-object detectors or by training a combined multiobject detection and classification system. In the medical domain, these models can be further improved by relying on strong spatial prior information present in medical images of a human body. This chapter describes how the spatial prior can be used to recognize multiple anatomical structures, which results in the integrated detection network. The structures are recognized sequentially, one by one, using optimal order such that the later recognitions can benefit from constr

Druh

O - Ostatní výsledky

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů