@article{
 title = {Edge foci interest points},
 type = {article},
 year = {2011},
 pages = {359-366},
 id = {f9454bcd-b3fb-3ed4-842c-1b41b3ca48ea},
 created = {2022-09-19T09:01:24.818Z},
 file_attached = {true},
 profile_id = {276016a7-2c9d-3507-8888-093db7c54774},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2022-09-19T09:03:16.768Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 folder_uuids = {02fb5526-03ff-44ad-8d5c-42bd496c3100},
 private_publication = {false},
 abstract = {In this paper, we describe an interest point detector using edge foci. Unlike traditional detectors that compute interest points directly from image intensities, we use normalized intensity edges and their orientations. We hypothesize that detectors based on the presence of oriented edges are more robust to non-linear lighting variations and background clutter than intensity based techniques. Specifically, we detect edge foci, which are points in the image that are roughly equidistant from edges with orientations perpendicular to the point. The scale of the interest point is defined by the distance between the edge foci and the edges. We quantify the performance of our detector using the interest point's repeatability, uniformity of spatial distribution, and the uniqueness of the resulting descriptors. Results are found using traditional datasets and new datasets with challenging non-linear lighting variations and occlusions. © 2011 IEEE.},
 bibtype = {article},
 author = {Zitnick, C. Lawrence and Ramnath, Krishnan},
 doi = {10.1109/ICCV.2011.6126263},
 journal = {Proceedings of the IEEE International Conference on Computer Vision}
}

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