On-line learning of shape information for object segmentation and tracking

John Chiverton, Majid Mirmehdi, Xianghua Xie

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

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    We present segmentation and tracking of deformable objects using non-linear on-line learning of high-level shape information in the form of a level set function. The emphasis is for successful tracking of objects that undergo smooth arbitrary deformations, but without the a priori learning of shape constraints. The high-level shape information is learnt on-line by defining a memory of object samples in a high-dimensional shape space. These shape samples are then used as weights via a locally defined shape space kernel function to define a template against which potential future shapes of the tracked object can be compared. Results for the successful tracking of a range of deformable motions are presented.
    Original languageEnglish
    Title of host publicationProceedings of the British Machine Vision Conference
    EditorsA. Cavallaro, S. Prince, D. Alexander
    PublisherBritish Machine Vision Association
    ISBN (Electronic)1901725391
    Publication statusPublished - 2009


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