Detail publikace

Framework for Research on Detection Classifiers

HRADIŠ, M.

Originální název

Framework for Research on Detection Classifiers

Anglický název

Framework for Research on Detection Classifiers

Jazyk

en

Originální abstrakt

Detection of patterns in images with classifiers is currently one of the most important research topics in computer vision. Many practical applications such as face detection exist and recent work even suggests that any specialized detectors (e.g. corner-point detectors) can be approximated by very fast detection classifiers. In this paper, we analyze the requirements on tools which are needed when experimenting with detection classifiers and we present a general framework which was created to fulfill these requirements. This framework offers high performance for training, high variability, elegant handling of configuration and it is able to meet all the requirements which arise when experimenting with almost all possible kinds of detection classifiers. The framework offers good testing support, full supporting infrastructure and some useful training algorithms and features. We offer this framework for research and educational purposes and we hope it will allow lower initial investments when experimenting with detection classifiers.

Anglický abstrakt

Detection of patterns in images with classifiers is currently one of the most important research topics in computer vision. Many practical applications such as face detection exist and recent work even suggests that any specialized detectors (e.g. corner-point detectors) can be approximated by very fast detection classifiers. In this paper, we analyze the requirements on tools which are needed when experimenting with detection classifiers and we present a general framework which was created to fulfill these requirements. This framework offers high performance for training, high variability, elegant handling of configuration and it is able to meet all the requirements which arise when experimenting with almost all possible kinds of detection classifiers. The framework offers good testing support, full supporting infrastructure and some useful training algorithms and features. We offer this framework for research and educational purposes and we hope it will allow lower initial investments when experimenting with detection classifiers.

Dokumenty

BibTex


@inproceedings{BUT27709,
  author="Michal {Hradiš}",
  title="Framework for Research on Detection Classifiers",
  annote="Detection of patterns in images with classifiers is currently one of the most
important research topics in computer vision. Many practical applications such as
face detection exist and recent work even suggests that any specialized detectors
(e.g. corner-point detectors) can be approximated by very fast detection
classifiers. In this paper, we analyze the requirements on tools which are needed
when experimenting with detection classifiers and we present a general framework
which was created to fulfill these requirements. This framework offers high
performance for training, high variability, elegant handling of configuration and
it is able to meet all the requirements which arise when experimenting with
almost all possible kinds of detection classifiers. The framework offers good
testing support, full supporting infrastructure and some useful training
algorithms and features. We offer this framework for research and educational
purposes and we hope it will allow lower initial investments when experimenting
with detection classifiers.",
  address="Comenius University in Bratislava",
  booktitle="Proceedings of Spring Conference on Computer Graphics",
  chapter="27709",
  edition="NEUVEDEN",
  howpublished="print",
  institution="Comenius University in Bratislava",
  year="2008",
  month="april",
  pages="171--177",
  publisher="Comenius University in Bratislava",
  type="conference paper"
}