Detail publikace

Automatic synthesis of classifiers in FGPA

KADLČEK, F. JURÁNEK, R. ZEMČÍK, P.

Originální název

Automatic synthesis of classifiers in FGPA

Anglický název

Automatic synthesis of classifiers in FGPA

Jazyk

en

Originální abstrakt

In this paper an approach to automatic synthesis of classifiers in FPGAs based on AdaBoost algorithm is introduced. The various boosting methods are widely used for detection of objects in images. In this paper a novel approach to automatic synthesis of a classifier with respect to an efficient use of computing resources is proposed. The main purpose is to achieve an acceptable trade-off between speed and resources consumption of the designed classifier. The features, used in boosting algorithms, are based on Local Binary Patterns, which are more suitable for hardware implementation then traditionally used Haar-like features. Automatic synthesis of classifiers from trained data sets is also described. In the paper there are also described the results achieved.

Anglický abstrakt

In this paper an approach to automatic synthesis of classifiers in FPGAs based on AdaBoost algorithm is introduced. The various boosting methods are widely used for detection of objects in images. In this paper a novel approach to automatic synthesis of a classifier with respect to an efficient use of computing resources is proposed. The main purpose is to achieve an acceptable trade-off between speed and resources consumption of the designed classifier. The features, used in boosting algorithms, are based on Local Binary Patterns, which are more suitable for hardware implementation then traditionally used Haar-like features. Automatic synthesis of classifiers from trained data sets is also described. In the paper there are also described the results achieved.

Dokumenty

BibTex


@inproceedings{BUT76352,
  author="Filip {Kadlček} and Roman {Juránek} and Pavel {Zemčík}",
  title="Automatic synthesis of classifiers in FGPA",
  annote="In this paper an approach to automatic synthesis of classifiers in FPGAs based on
AdaBoost algorithm is introduced. The various boosting methods are widely used
for detection of objects in images. In this paper a novel approach to automatic
synthesis of a classifier with respect to an efficient use of computing resources
is proposed. The main purpose is to achieve an acceptable trade-off between speed
and resources consumption of the designed classifier. The features, used in
boosting algorithms, are based on Local Binary Patterns, which are more suitable
for hardware implementation then traditionally used Haar-like features. Automatic
synthesis of classifiers from trained data sets is also described. In the paper
there are also described the results achieved.",
  address="Tomas Bata University in Zlín",
  booktitle="International Bata Conference for Ph.D. Students and Young Researchers",
  chapter="76352",
  edition="NEUVEDEN",
  howpublished="print",
  institution="Tomas Bata University in Zlín",
  year="2011",
  month="april",
  pages="1--12",
  publisher="Tomas Bata University in Zlín",
  type="conference paper"
}