Graphics Reference
In-Depth Information
[325] K. Mikolajczyk and C. Schmid. Indexing based on scale invariant interest points. In
IEEE
International Conference on Computer Vision (ICCV)
, 2001.
[326] K. Mikolajczyk and C. Schmid. An affine invariant interest point detector. In
European
Conference on Computer Vision (ECCV)
, 2002.
[327] K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors.
International Journal of Computer Vision
, 60(1):63-86, Oct. 2004.
[328] K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors.
IEEE
Transactions on Pattern Analysis and Machine Intelligence
, 27(10):1615-30, Oct. 2005.
[329] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir,
and L. Van Gool. A comparison of affine region detectors.
International Journal of
Computer Vision
, 65(1):43-72, Nov. 2005.
[330] F. Mindru, T. Tuytelaars, L. Van Gool, and T. Moons. Moment invariants for recognition
under changing viewpoint and illumination.
Computer Vision and Image Understanding
,
94(1-3):3-27, Apr.-Jun. 2004.
[331] T. B. Moeslund and E. Granum. A survey of computer vision-based human motion
capture.
Computer Vision and Image Understanding
, 81(3):231-68, Mar. 2001.
[332] T. B. Moeslund, A. Hilton, and V. Krüger. A survey of advances in vision-based human
motion capture and analysis.
Computer Vision and Image Understanding
, 104(2-3):90-
126, Nov. 2006.
[333] P. Montesinos, V. Gouet, R. Deriche, and D. Pelé. Matching color uncalibrated images
using differential invariants.
Image and Vision Computing
, 18(9):659-71, June 2000.
[334] R. Morano, C. Ozturk, R. Conn, S. Dubin, S. Zietz, and J. Nissano. Structured light using
pseudorandom codes.
IEEE Transactions on Pattern Analysis and Machine Intelligence
,
20(3):322-7, Mar. 1998.
[335] H. Moravec. Obstacle avoidance and navigation in the real world by a seeing robot rover.
Technical Report CMU-RI-TR-3, Carnegie Mellon University, 1980.
[336] P. Moreels and P. Perona. Evaluation of features detectors and descriptors based on 3D
objects.
International Journal of Computer Vision
, 73(3):263-84, July 2007.
[337] D. Morris, K. Kanatani, and T. Kanade. Uncertainty modeling for optimal structure from
motion. In B. Triggs, A. Zisserman, and R. Szeliski, editors,
Vision Algorithms: Theory and
Practice
, pages 315-45. Springer, 2000.
[338] D. Morris, K. Kanatani, and T. Kanade. Gauge fixing for accurate 3D estimation. In
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)
,
2001.
[339] E. Mortensen and W. Barrett. Interactive segmentation with intelligent scissors.
Graphi-
cal Models and Image Processing
, 60(5):349-84, Sept. 1998.
[340] E. Mouragnon, M. Lhuillier, M. Dhome, F. Dekeyser, and P. Sayd. Generic and real-time
structure from motion using local bundle adjustment.
Image and Vision Computing
,
27(8):1178-93, July 2009.
[341] K. Murphy, Y. Weiss, andM. Jordan. Loopy belief propagation for approximate inference:
An empirical study. In
Uncertainty in AI
, 1999.
[342] R.M.Murray, Z. Li, andS. S. Sastry.
AMathematical Introduction toRoboticManipulation
.
CRC Press, 1994.
[343] H.-H. Nagel and W. Enkelmann. An investigation of smoothness constraints for the esti-
mationof displacement vector fields fromimage sequences.
IEEETransactions onPattern
Analysis and Machine Intelligence
, PAMI-8(5):565-93, Sept. 1986.
[344] Y. Nakamura, T. Matsuura, K. Satoh, and Y. Ohta. Occlusion detectable stereo-occlusion
patterns in camera matrix. In
IEEE Computer Society Conference on Computer Vision and
Pattern Recognition (CVPR)
, 1996.
[345] S. Narasimhan, S. Nayar, B. Sun, and S. Koppal. Structured light in scattering media. In
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)
,
2005.
[346] S. Negahdaripour. Revised definition of optical flow: integration of radiometric and
geometric cues for dynamic scene analysis.
IEEE Transactions on Pattern Analysis and
Machine Intelligence
, 20(9):961-79, Sept. 1998.
