Multiple regression estimation for motion analysis and segmentation

Vladimir Cherkassky, Yunqian Ma, Harry Wechsler

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

1 Scopus citations

Abstract

This paper describes multiple model estimation for motion analysis and segmentation (oka spatial partitioning), from point correspondences in two successive images. In motion analysis applications, available (training) data is generated by several unknown models (motions). However, the correspondence between data samples and different models (motions) is unknown. Hence, the goal of learning (motion estimation) is two-fold, i.e. estimation (learning) of unknown motions (models) and separation (segmentation) of available data into several subsets corresponding to different motions. We present the mathematical formulation for multiple motion estimation, as a problem of learning several (regression) mappings, from a single data set, and then show a constructive (SVM-based) learning algorithm developed for this setting. Experimental results show potential advantages of the proposed method.

Original languageEnglish (US)
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages2547-2552
Number of pages6
DOIs
StatePublished - Dec 1 2004
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: Jul 25 2004Jul 29 2004

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume4
ISSN (Print)1098-7576

Other

Other2004 IEEE International Joint Conference on Neural Networks - Proceedings
CountryHungary
CityBudapest
Period7/25/047/29/04

Fingerprint Dive into the research topics of 'Multiple regression estimation for motion analysis and segmentation'. Together they form a unique fingerprint.

Cite this