Local Intensity Order Pattern for feature description

By Zhenhua Wang, Bin Fan, Fuchao Wu. Vision, 2011

347 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1c2e9d5979299b166e4d822112ffd3fbf6278f70

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Evaluation of local detectors and descriptors for fast feature matching (2012, methodology): “This concept is further exploited in LIOP [17] together with segmentation based location grid in contrast to 4× 4 regular grid in SIFT or SURF.”
LIFT: Learned Invariant Feature Transform (2016, methodology): “…SURF [2], KAZE [33], ORB [4], Daisy [34] with SIFT detector, sGLOH [35] with Harris-aﬃne detector [36], MROGH [37] with Harris-aﬃne detector, LIOP [38] with Harris-aﬃne detector, BiCE [39] with Edge Foci detector [19], BRISK [40], FREAK [41] with BRISK detector, VGG [23] (trained on ‘Liberty’)…”
TILDE: A Temporally Invariant Learned DEtector (2014, background): “This is a severe handicap when attempting to match images taken in fair and foul weather, in the morning and evening, in winter and summer, even with illumination invariant descriptors [13, 39, 14, 43].”
SOSNet: Second Order Similarity Regularization for Local Descriptor Learning (2019, background): “Early works on local patch description focused on low level processes such as gradient filters and intensity comparisons, including SIFT [21], GLOH [24], DAISY [39], DSP-SIFT [12] and LIOP [38].”
L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space (2017, methodology): “Besides learned descriptors, we use LIOP [23] as the baseline of handcrafted descriptors, since it was reported to surpass most of the handcrafted descriptors on this dataset.”
Towards Reversal-Invariant Image Representation (2017, background): “These pieces of work inspire us that symmetry is the key to reversal invariance (Skelly and Sclaroff 2007; Wang et al. 2011).”
Discriminative Learning of Deep Convolutional Feature Point Descriptors (2015, background): “While most descriptors, such as SIFT [16], rely on hand-crafted features [1, 13, 16, 22, 27, 28, 32], there has recently been interest in using machine learning algorithms to learn them from large datasets [20, 23, 29].”
Image Matching from Handcrafted to Deep Features: A Survey (2020, methodology): “LIOP was further improved intoOIOP/MIOP (Wang et al. 2015), which can then encode overall ordinal information for noise and distortion robustness.”