Information Theoretic Rotationwise Robust Binary Descriptor Learning
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[1] Matti Pietikäinen,et al. A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..
[2] Roland Siegwart,et al. BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.
[3] D. Lowe,et al. Fast Matching of Binary Features , 2012, 2012 Ninth Conference on Computer and Robot Vision.
[4] Gang Hua,et al. Discriminant Embedding for Local Image Descriptors , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[5] Piotr Indyk,et al. Approximate nearest neighbors: towards removing the curse of dimensionality , 1998, STOC '98.
[6] Yan Ke,et al. PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[7] Ethan Rublee,et al. ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.
[8] Pierre Vandergheynst,et al. FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[9] Pascal Fua,et al. Do We Need Binary Features for 3D Reconstruction? , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[10] Cordelia Schmid,et al. Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.
[11] Nikos Komodakis,et al. Learning to compare image patches via convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[13] Cordelia Schmid,et al. A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Adrien Bartoli,et al. Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces , 2013, BMVC.
[15] Ramin Zabih,et al. Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.
[16] Pascal Monasse,et al. Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion , 2013, ICCV.
[17] Xin Yang,et al. LDB: An ultra-fast feature for scalable Augmented Reality on mobile devices , 2012, 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).
[18] Jean-Michel Morel,et al. Comparing feature detectors: A bias in the repeatability criteria , 2014, 2015 IEEE International Conference on Image Processing (ICIP).
[19] Bin Fan,et al. Local Intensity Order Pattern for feature description , 2011, 2011 International Conference on Computer Vision.
[20] Krystian Mikolajczyk,et al. BOLD - Binary online learned descriptor for efficient image matching , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Matthew A. Brown,et al. Automatic Panoramic Image Stitching using Invariant Features , 2007, International Journal of Computer Vision.
[22] Vincent Lepetit,et al. Boosting Binary Keypoint Descriptors , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[23] Vincent Lepetit,et al. BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.
[24] Christopher Hunt,et al. Notes on the OpenSURF Library , 2009 .
[25] Gavin Brown,et al. Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood , 2014, S+SSPR.
[26] Fuhui Long,et al. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.