Detail publikace

Local Rank Patterns - Novel Features for Rapid Object Detection

HRADIŠ, M. HEROUT, A. ZEMČÍK, P.

Originální název

Local Rank Patterns - Novel Features for Rapid Object Detection

Anglický název

Local Rank Patterns - Novel Features for Rapid Object Detection

Jazyk

en

Originální abstrakt

This paper presents Local Rank Patterns (LRP) - novel features for rapid object detection in images which are based on existing features Local Rank Differences (LRD). The performance of the novel features is thoroughly tested on frontal face detection task and it is compared to the performance of the LRD and the traditionally used Haar-like features. The results show that the LRP surpass the LRD and the Haar-like features in the precision of detection and also in the average number of features needed for classification. Considering recent successful and efficient implementations of LRD on CPU, GPU and FPGA, the results suggest that LRP are good choice for object detection and that they could replace the Haar-like features in some applications in the future.

Anglický abstrakt

This paper presents Local Rank Patterns (LRP) - novel features for rapid object detection in images which are based on existing features Local Rank Differences (LRD). The performance of the novel features is thoroughly tested on frontal face detection task and it is compared to the performance of the LRD and the traditionally used Haar-like features. The results show that the LRP surpass the LRD and the Haar-like features in the precision of detection and also in the average number of features needed for classification. Considering recent successful and efficient implementations of LRD on CPU, GPU and FPGA, the results suggest that LRP are good choice for object detection and that they could replace the Haar-like features in some applications in the future.

Dokumenty

BibTex


@inproceedings{BUT33447,
  author="Michal {Hradiš} and Adam {Herout} and Pavel {Zemčík}",
  title="Local Rank Patterns - Novel Features for Rapid Object Detection",
  annote="This paper presents Local Rank Patterns (LRP) - novel features for rapid object
detection in images which are based on existing features Local Rank Differences
(LRD). The performance of the novel features is thoroughly tested on frontal face
detection task and it is compared to the performance of the LRD and the
traditionally used Haar-like features. The results show that the LRP surpass the
LRD and the Haar-like features in the precision of detection and also in the
average number of features needed for classification. Considering recent
successful and efficient implementations of LRD on CPU, GPU and FPGA, the results
suggest that LRP are good choice for object detection and that they could replace
the Haar-like features in some applications in the future.",
  address="Springer Verlag",
  booktitle="Proceedings of International Conference on Computer Vision and Graphics 2008",
  chapter="33447",
  edition="Lecture Notes in Computer Science",
  howpublished="print",
  institution="Springer Verlag",
  journal="Lecture Notes in Computer Science (IF 0,513)",
  number="12",
  year="2008",
  month="november",
  pages="1--12",
  publisher="Springer Verlag",
  type="conference paper"
}