Difference between revisions of "Booklist: computer vision"

From DataScienceAndEngineeringWiki
Jump to: navigation, search
(+ Add booklist for computer vision.)
 
(~ Use ACM URL.)
 
Line 1: Line 1:
# David G. Lowe, Object Recognition from Local Scale-Invariant Features, 1999, http://www.cs.ubc.ca/~lowe/papers/iccv99.pdf
+
# David G. Lowe, Object Recognition from Local Scale-Invariant Features, 1999, http://dl.acm.org/citation.cfm?id=851523
 
# Johnson, Herbert, Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes, 1999, http://dl.acm.org/citation.cfm?id=302947
 
# Johnson, Herbert, Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes, 1999, http://dl.acm.org/citation.cfm?id=302947
 
# Malik et al, Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons, 1999, http://dl.acm.org/citation.cfm?id=543017
 
# Malik et al, Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons, 1999, http://dl.acm.org/citation.cfm?id=543017

Latest revision as of 17:21, 26 July 2017

  1. David G. Lowe, Object Recognition from Local Scale-Invariant Features, 1999, http://dl.acm.org/citation.cfm?id=851523
  2. Johnson, Herbert, Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes, 1999, http://dl.acm.org/citation.cfm?id=302947
  3. Malik et al, Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons, 1999, http://dl.acm.org/citation.cfm?id=543017
  4. Lazebnik et al, A Sparse Texture Representation Using Local Affine Regions, 2004, http://dl.acm.org/citation.cfm?id=1070813
  5. Mikolajczyk et al, A Performance Evaluation of Local Descriptors, 2005, http://dl.acm.org/citation.cfm?id=1083989
  6. Dalal et al, Histograms of Oriented Gradients for Human Detection, 2005, http://dl.acm.org/citation.cfm?id=1069007
  7. Bay et al, Speeded-Up Robust Features (SURF), 2008, http://dl.acm.org/citation.cfm?id=1370556
  8. Krizhevsky et al, ImageNet classification with deep convolutional neural networks, 2012, http://dl.acm.org/citation.cfm?id=2999257