Robust Template Matching Using Scale-Adaptive Deep Convolutional Features

Cited 8 time in webofscience Cited 0 time in scopus
  • Hit : 420
  • Download : 0
In this paper, we propose a deep convolutional feature-based robust and efficient template matching method. The originality of the proposed method is that it is based on a scale-adaptive feature extraction approach. This approach is influenced by an observation that each layer in a CNN represents a different level of deep features of the actual image contents. In order to keep the features scalable, we extract deep feature vectors of the template and the input image adaptively from a layer of a CNN. By using such scalable and deep representation of the image contents, we attempt to solve the template matching by measuring the similarity between the features of the template and the input image using an efficient similarity measuring technique called normalized cross-correlation (NCC). Using NCC helps in avoiding redundant computations of adjacent patches caused by the sliding window approach. As a result, the proposed method achieves state-of-the-art template matching performance and lowers the computational cost significantly than the state-of-the-art methods in the literature.
Publisher
Asia-Pacific Signal and Information Processing Association(APSIPA)
Issue Date
2017-12-14
Language
English
Citation

9th Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC), pp.708 - 711

ISSN
2309-9402
DOI
10.1109/APSIPA.2017.8282124
URI
http://hdl.handle.net/10203/227233
Appears in Collection
EE-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 8 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0