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Adaptive Contour Features in Oriented Granular Space for Human Detection and Segmentation
 

Summary: Adaptive Contour Features in Oriented Granular Space for Human Detection
and Segmentation
Wei Gao, Haizhou Ai
Computer Science and Technology Department
Tsinghua University, Beijing, P.R. China
ahz@mail.tsinghua.edu.cn
Shihong Lao
Core Technology Center
Omron Corporation, Kyoto, Japan
lao@ari.ncl.omron.co.jp
Abstract
In this paper, a novel feature named Adaptive Contour
Feature (ACF) is proposed for human detection and seg-
mentation. This feature consists of a chain of a number of
granules in Oriented Granular Space (OGS) that is learnt
via the AdaBoost algorithm. Three operations are defined
on the OGS to mine object contour feature and feature co-
occurrences automatically. A heuristic learning algorithm
is proposed to generate an ACF that at the same time define
a weak classifier for human detection or segmentation. Ex-

  

Source: Ai, Haizhou - Department of Computer Science and Technology, Tsinghua University

 

Collections: Computer Technologies and Information Sciences