Human figure segmentation using independent component analysis

G Rogez, C Orrite-Urunuela, J Martinez-del-Rincon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we present a Statistical Shape Model for Human Figure Segmentation in gait sequences. Point Distribution Models (PDM) generally use Principal Component analysis (PCA) to describe the main directions of variation in the training set. However, PCA assumes a number of restrictions on the data that do not always hold. In this work, we explore the potential of Independent Component Analysis (ICA) as an alternative shape decomposition to the PDM-based Human Figure Segmentation. The shape model obtained enables accurate estimation of human figures despite segmentation errors in the input silhouettes and has really good convergence qualities.

Original languageEnglish
Title of host publicationPATTERN RECOGNITION AND IMAGE ANALYSIS, PT 1, PROCEEDINGS
EditorsJS Marques, N PerezdelaBlanca, P Pina
Place of PublicationBERLIN
PublisherSpringer
Pages300-307
Number of pages8
ISBN (Print)3-540-26153-2
Publication statusPublished - 2005
Event2nd Iberian Conference on Pattern Recongnition and Image Analysis - Estoril, Spain
Duration: 07 Jun 200509 Jun 2005

Conference

Conference2nd Iberian Conference on Pattern Recongnition and Image Analysis
CountrySpain
CityEstoril
Period07/06/200509/06/2005

Cite this

Rogez, G., Orrite-Urunuela, C., & Martinez-del-Rincon, J. (2005). Human figure segmentation using independent component analysis. In JS. Marques, N. PerezdelaBlanca, & P. Pina (Eds.), PATTERN RECOGNITION AND IMAGE ANALYSIS, PT 1, PROCEEDINGS (pp. 300-307). Springer.