TailoredBRIEF: Online Per-Feature Descriptor Customization

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015

PDF thumbnail
(PDF, 561.4 KB )


Image feature descriptors composed of a series of binary intensity comparisons yield substantial memory and runtime improvements over conventional descriptors, but are sensitive to viewpoint changes in ways that vary per feature. We propose a method to improve the matching performance of such descriptors by specifically reasoning about the reliability of test results on a feature-by-feature basis. We demonstrate an intuitive method to learn improved descriptor structures for individual features. Further, these learned results can be efficiently applied during matching with little increase in runtime. We provide an evaluation using a standard, ground-truthed, multi-image dataset.


    AUTHOR     = {Andrew Richardson and Edwin Olson},
    TITLE      = {{TailoredBRIEF}: {Online Per-Feature Descriptor Customization}},
    BOOKTITLE  = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent
                 Robots and Systems {(IROS)}},
    YEAR       = {2015},
    MONTH      = {September},
    ADDRESS    = {Hamburg, Germany},