LACBoost and FisherBoost: Optimally Building Cascade Classifiers

Shen, Chunhua; Wang, Peng; Li, Hanxi

Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

LACBoost and FisherBoost: Optimally Building Cascade Classifiers

Date

2010

Authors

Shen, Chunhua

Wang, Peng

Li, Hanxi

Publisher

Springer

Abstract

Object detection is one of the key tasks in computer vision. The cascade framework of Viola and Jones has become the de facto standard. A classifier in each node of the cascade is required to achieve extremely high detection rates, instead of low overall

URI

http://hdl.handle.net/1885/62570

Collections

ANU Research Publications

Source

Proceedings of the European Conference on Computer Vision (ECCV 2010)

Type

Conference paper

DOI

10.1007/978-3-642-15552-9_44

Restricted until

2037-12-31

Downloads

File

Description

01_Shen_LACBoost_and_FisherBoost:_2010.pdf (294.55 KB)

Full item page

Cultural advice

LACBoost and FisherBoost: Optimally Building Cascade Classifiers

Date

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

Keywords

Citation

URI

Collections

Source

Type

Book Title

Entity type

Access Statement

License Rights

DOI

Restricted until

Downloads