Robust and accurate shape model fitting using random forest regression voting

Tim F. Cootes, Mircea C. Ionita, Claudia Lindner, Patrick Sauer

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    A widely used approach for locating points on deformable objects is to generate feature response images for each point, then to fit a shape model to the response images. We demonstrate that Random Forest regression can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position. We show this leads to fast and accurate matching when combined with a statistical shape model. We evaluate the technique in detail, and compare with a range of commonly used alternatives on several different datasets. We show that the random forest regression method is significantly faster and more accurate than equivalent discriminative, or boosted regression based methods trained on the same data. © 2012 Springer-Verlag.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    Pages278-291
    Number of pages13
    Volume7578
    DOIs
    Publication statusPublished - 2012
    Event12th European Conference on Computer Vision, ECCV 2012 - Florence
    Duration: 1 Jul 2012 → …

    Conference

    Conference12th European Conference on Computer Vision, ECCV 2012
    CityFlorence
    Period1/07/12 → …

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