In earlier work, we proposed treating wide baseline matching of feature points as a classification problem, in which each class corresponds to the set of all possible views of such a point. We used a K-mean plus Nearest Neighbor classifier to validate our approach, mostly because it was simple to implement. It has proved effective but still too slow for real-time use. In this paper, we advocate instead the use of randomized trees as the classification technique. It is both fast enough for real-time performance and more robust. It also gives us a principled way not only to match keypoints but to select during a training phase those that are the most recognizable ones. This results in a real-time system able to detect and position in 3D planar, non-planar, and even deformable objects. It is robust to illuminations changes, scale changes and occlusions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Randomized trees for real-time keypoint recognition


    Contributors:
    Lepetit, V. (author) / Lagger, P. (author) / Fua, P. (author)


    Publication date :

    2005-01-01


    Size :

    938123 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Local Descriptor by Zernike Moments for Real-Time Keypoint Matching

    Hwang, Sun-Kyoo / Billinghurst, Mark / Kim, Whoi-Yul | IEEE | 2008


    Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition

    Mian, A. S. / Bennamoun, M. / Owens, R. | British Library Online Contents | 2008


    KEYPOINT BASED ACTION LOCALIZATION

    KADAV ASIM / LAI FARLEY / GRAF HANS PETER et al. | European Patent Office | 2022

    Free access

    Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition

    Guo, J. / Shi, C. / Azzopardi, G. et al. | British Library Online Contents | 2016


    Performance Evaluation of 3D Keypoint Detectors

    Tombari, F. / Salti, S. / Di Stefano, L. | British Library Online Contents | 2013