@article{
 title = {DNN Flow: DNN Feature Pyramid based Image Matching},
 type = {article},
 year = {2015},
 identifiers = {[object Object]},
 pages = {109.1-109.10},
 id = {cc7c5155-5730-3538-8cc9-9503fa44b984},
 created = {2019-08-06T06:22:16.347Z},
 file_attached = {false},
 profile_id = {0d5bdafc-dbf1-3d6c-a8b9-dbbfba9e5635},
 group_id = {3fcca934-7672-3a39-8709-ace064596b2a},
 last_modified = {2019-08-06T06:22:16.347Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2014. The copyright of this document resides with its authors. Image matching especially in category level is a challenge but important problem in vision. The advance of image matching largely depends on the advance of image features. In viewing recent success of learned image feature by DNN, we propose an image matching algorithm based on DNN feature pyramid, named as DNN Flow. The nature of DNN feature pyramid in detecting different level patterns makes it is suitable to match two images in a coarse to fine manner, where top level coarsely matches two images in object level, middle level matches two images in part level, and low level finely matches two images in pixel level. The coarse to fine matching based on DNN feature pyramid is formulated as a series of optimization problems considering the guidance from top level. Extensive experiments demonstrate the superiority of DNN Flow in image matching under challenge variations.},
 bibtype = {article},
 author = {Yu, Wei and Yang, Kuiyuan and Bai, Yalong and Yao, Hongxun and Rui, Yong}
}

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