Detail publikace

Low-Level Image Features for Real-Time Object Detection

Originální název

Low-Level Image Features for Real-Time Object Detection

Anglický název

Low-Level Image Features for Real-Time Object Detection

Jazyk

en

Originální abstrakt

The main aim of the chapter is to provide information about the Local Rank Patterns image feature: Its background, mathematical definition, evaluation of its performance and notes on its implementation and use in object detectors. Implementations on the MMX, SSE, FPGA (programmable hardware), GPU (Cg) and CUDA platforms are described and experimentally evaluated. The performance of the image feature is evaluated within the WaldBoost classifier on the task of face detection, and it is compared to the commonly used Haar wavelets, local binary patterns and other low level features. The Local Rank Patterns feature seems suitable for hardware acceleration both directly by programmable or hard-wired hardware, but also by processors supporting different sets of the SIMD instructions. It is shown, that the LRP feature is an important alternative for construction of fast object detectors.

Anglický abstrakt

The main aim of the chapter is to provide information about the Local Rank Patterns image feature: Its background, mathematical definition, evaluation of its performance and notes on its implementation and use in object detectors. Implementations on the MMX, SSE, FPGA (programmable hardware), GPU (Cg) and CUDA platforms are described and experimentally evaluated. The performance of the image feature is evaluated within the WaldBoost classifier on the task of face detection, and it is compared to the commonly used Haar wavelets, local binary patterns and other low level features. The Local Rank Patterns feature seems suitable for hardware acceleration both directly by programmable or hard-wired hardware, but also by processors supporting different sets of the SIMD instructions. It is shown, that the LRP feature is an important alternative for construction of fast object detectors.

BibTex


@inbook{BUT55167,
  author="Adam {Herout} and Pavel {Zemčík} and Michal {Hradiš} and Roman {Juránek} and Jiří {Havel} and Radovan {Jošth} and Martin {Žádník}",
  title="Low-Level Image Features for Real-Time Object Detection",
  annote="The main aim of the chapter is to provide information about the Local Rank
Patterns image feature: Its background, mathematical definition, evaluation of
its performance and notes on its implementation and use in object detectors.
Implementations on the MMX, SSE, FPGA (programmable hardware), GPU (Cg) and CUDA
platforms are described and experimentally evaluated. The performance of the
image feature is evaluated within the WaldBoost classifier on the task of face
detection, and it is compared to the commonly used Haar wavelets, local binary
patterns and other low level features. The Local Rank Patterns feature seems
suitable for hardware acceleration both directly by programmable or hard-wired
hardware, but also by processors supporting different sets of the SIMD
instructions. It is shown, that the LRP feature is an important alternative for
construction of fast object detectors.",
  address="IN-TECH Education and Publishing",
  booktitle="Pattern Recognition, Recent Advances",
  chapter="55167",
  edition="NEUVEDEN",
  howpublished="print",
  institution="IN-TECH Education and Publishing",
  year="2010",
  month="february",
  pages="111--136",
  publisher="IN-TECH Education and Publishing",
  type="book chapter"
}