This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Recognizing Articulated Objects Using a Region-Based Invariant Transform
October 2005 (vol. 27 no. 10)
pp. 1660-1665
ASCII Text
x 
 
Isaac Weiss, Manjit Ray, "Recognizing Articulated Objects Using a Region-Based Invariant Transform," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1660-1665, October, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.208,
author = {Isaac Weiss and Manjit Ray},
title = {Recognizing Articulated Objects Using a Region-Based Invariant Transform},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {10},
issn = {0162-8828},
year = {2005},
pages = {1660-1665},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.208},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Recognizing Articulated Objects Using a Region-Based Invariant Transform
IS - 10
SN - 0162-8828
SP1660
EP1665
EPD - 1660-1665
A1 - Isaac Weiss,
A1 - Manjit Ray,
PY - 2005
KW - Index Terms- Object recognition
KW - invariance
KW - range images
KW - transform.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
In this paper, we present a new method for representing and recognizing objects, based on invariants of the object's regions. We apply the method to articulated objects in low-resolution, noisy range images. Articulated objects such as a back-hoe can have many degrees of freedom, in addition to the unknown variables of viewpoint. Recognizing such an object in an image can involve a search in a high-dimensional space that involves all these unknown variables. Here, we use invariance to reduce this search space to a manageable size. The low resolution of our range images makes it hard to use common features such as edges to find invariants. We have thus developed a new "featureless” method that does not depend on feature detection. Instead of local features, we deal with whole regions of the object. We define a "transform” that converts the image into an invariant representation on a grid, based on invariant descriptors of entire regions centered around the grid points. We use these region-based invariants for indexing and recognition. While the focus here is on articulation, the method can be easily applied to other problems such as the occlusion of fixed objects.

[1] P.J. Besl and R.C. Jain, “Segmentation through Variable-Order Surface Fitting,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 10, pp. 167-192, 1988.
[2] T.O. Binford, “Visual Perception by Computer,” Proc. IEEE Conf. Systems and Control, 1971.
[3] A. Bruckstein, E. Rivlin, and I. Weiss, “Scale Space Invariants for Recognition,” Machine Vision and Applications, vol. 15, pp. 335-344, 1997.
[4] A.E. Johnson and M. Hebert, “Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp. 433-449, May 1999.
[5] D. Keren, R. Rivlin, I. Shimshoni, and I. Weiss, “Recognizing 3D Objects Using Tactile Sensing and Curve Invariants,” J. Math. Imaging and Vision, vol. 12, no. 1, pp. 5-23, Feb. 2000.
[6] C.B. Moler and G.W. Stewart, “An Algorithm for Generalized Matrix Eigenvalue Problems,” SIAM J. Numerical Analysis, vol. 10, 1973.
[7] G. Mori, S. Belongie, and J. Malik, “Shape Contexts Enable Efficient Retrieval of Similar Shapes,” Proc. Conf. Computer Vision and Pattern Recognition, pp. 723-730, 2001.
[8] Geometric Invariance in Machine Vision. J.L. Mundy and A. Zisserman, eds. Cambridge, Mass.: MIT Press, 1992.
[9] D. Renaudie, D. Kriegman, and J. Ponce, “Duals, Invariants, and the Recognition of Smooth Objects from Their Occluding Contour,” Proc. European Conf. Computer Vision, 2000.
[10] E. Rivlin and I. Weiss, “Local Invariants for Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 3, pp. 226-238, Mar. 1995.
[11] F. Rothganger, S. Lazebnik, C. Schmid, and J. Ponce, “3D Object Recognition and Modeling Using Affine-Invariant Patches and Multi-View Spatial constraints,” Proc. Int'l Conf. Computer Vision, Oct. 2003.
[12] S. Ruiz-Correa, L.G. Shapiro, and M. Meilva, “A New Paradigm for Recognizing 3D Object Shapes from Range Data,” Proc. Int'l Conf. Computer Vision, Oct. 2003.
[13] F. Stein and G. Medioni, “Structural Indexing: Efficient 3D Object Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 125-145, Feb. 1992.
[14] I. Weiss, “Projective Invariants of Shape,” Proc. Computer Vision and Image Processing, pp. 291-297, 1988.
[15] I. Weiss, “Noise Resistant Invariants of Curves,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 943-948, Sept. 1993.
[16] I. Weiss, “Geometric Invariants and Object Recognition,” Int'l J. Computer Vision, vol. 10, pp. 207-231, 1993.
[17] I. Weiss, “Model-Based Recognition of 3D Curves from One View,” J. Math. Imaging and Vision, vol. 10, pp. 1-10, 1999.
[18] I. Weiss and M. Ray, “Model-Based Recognition of 3D Objects from Single Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 116-128, Feb. 2001.
[19] M.D. Wheeler and K. Ikeuchi, “Sensor Modeling, Probabilistic Hypothesis Generation, and Robust Localization for Object Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 3, pp. 252-265, Mar. 1995.

Index Terms:
Index Terms- Object recognition, invariance, range images, transform.
Citation:
Isaac Weiss, Manjit Ray, "Recognizing Articulated Objects Using a Region-Based Invariant Transform," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1660-1665, Oct. 2005, doi:10.1109/TPAMI.2005.208
Usage of this product signifies your acceptance of the Terms of Use.