13.1.2.4 Invariants, Areas

Chapter Contents (Back)
Matching, Regions. Invariants, Areas.

Akaike, H.[Hirotugu],
A new look at the statistical model identification,
AC(19), No. 6, 1974, pp. 716-723.
IEEE DOI 1001
Measurement of goodness of fit to a statistical model. BibRef

Nielsen, L.[Lars], Sparr, G.[Gunnar],
Projective Area-Invariants as an Extension of the Cross-Ratio,
CVGIP(54), No. 1, July 1991, pp. 145-159.
WWW Link. BibRef 9107
Earlier: A2, A1:
Shape and mutual cross-ratios with applications to exterior, interior and relative orientation,
ECCV90(607-609).
Springer DOI 9004
BibRef

Kanatani, K.[Kenichi],
Computational Cross Ratio for Computer Vision,
CVGIP(60), No. 3, November 1994, pp. 371-381.
WWW Link. BibRef 9411

Kanatani, K.[Kenichi],
Statistical Foundation for Hypothesis Testing of Image Data,
CVGIP(60), No. 3, November 1994, pp. 382-391.
WWW Link. BibRef 9411

Kanatani, K.[Kenichi],
Geometric Information Criterion for Model Selection,
IJCV(26), No. 3, March 1998, pp. 171-189.
DOI Link 9804
See also Statistical-Analysis of Geometric Computation. BibRef

Kanatani, K.[Kenichi],
Uncertainty Modeling and Model Selection for Geometric Inference,
PAMI(26), No. 10, October 2004, pp. 1307-1319.
IEEE Abstract. 0409
Discuss the meaning of statistical methods for geometric inference. Feature uncertainty from image processing operations. Derive the geometric AIC and the geometric MDL as counterparts of Akaike's AIC ( See also new look at the statistical model identification, A. ) and Rissanen's MDL ( See also Universal Prior for Integers and Esitmation by Minimum Description Length, A. ). BibRef

Kanatani, K.[Kenichi],
Geometric BIC,
IEICE(E93-D), No. 1, January 2010, pp. 144-151.
WWW Link. 1001
Geometric fitting. Similar to geometric MDL. BibRef

Kanatani, K.[Kenichi],
Further improving geometric fitting,
3DIM05(2-13).
IEEE DOI 0508
BibRef

Sapiro, G., Tannenbaum, A.,
Area and Length Preserving Geometric Invariant Scale-Spaces,
PAMI(17), No. 1, January 1995, pp. 67-72.
IEEE DOI BibRef 9501
Earlier: ECCV94(B:449-458).
Springer DOI See also Affine Invariant Scale-Space. BibRef

Maybank, S.J.,
Probabilistic Analysis of the Application of the Cross Ratio to Model-Based Vision: Misclassification,
IJCV(14), No. 3, April 1995, pp. 199-210.
Springer DOI BibRef 9504

Maybank, S.J.,
Probabilistic Analysis of the Application of the Cross Ratio to Model-Based Vision,
IJCV(16), No. 1, September 1995, pp. 5-33.
Springer DOI Evaluation, Cross Ratio. Analysis of the use of the cross ratio for matching. How does it vary when the points have Gaussian distributions. BibRef 9509

Maybank, S.J.,
Stochastic Properties of the Cross Ratio,
PRL(17), No. 3, March 6 1996, pp. 211-217. BibRef 9603

Maybank, S.J.[Steven J.],
Relation Between 3D Invariants and 2D Invariants,
IVC(16), No. 1, January 30 1998, pp. 13-20.
WWW Link. 9803
BibRef
Earlier: RVS95(xx). BibRef

Maybank, S.J.[Steven J.],
Error Trade-Offs for the Cross-Ratio in Model Based Vision,
ConferenceWorkshop on Computer Vision for Space Applications 1993, pp. 350-359. Antibes France. BibRef 9300

Maybank, S.J.[Steven J.], Fraile, R.,
Minimum description length method for facet matching,
PRAI(14), 2000, pp. 919-927. BibRef 0001

Bribiesca, E.[Ernesto],
Measuring 3-D Shape Similarity Using Progressive Transformations,
PR(29), No. 7, July 1996, pp. 1117-1129.
WWW Link. 9607
Use Voxel representation. Measure how much "information" in the representation. Shape difference is how much work to trransform one to the other. See also easy measure of compactness for 2D and 3D shapes, An. See also Digital Elevation Model Data Analysis Using the Contact Surface Area. BibRef

Sanchez-Cruz, H.[Hermilo], Bribiesca, E.[Ernesto],
A method of optimum transformation of 3D objects used as a measure of shape dissimilarity,
IVC(21), No. 12, November 2003, pp. 1027-1036.
WWW Link. 0310
BibRef

Chetverikov, D., Lerch, A.,
A Matching Algorithm for Motion Analysis of Dense Populations,
PRL(11), 1990, pp. 743-749. BibRef 9000

Chetverikov, D., Lerch, A.,
A Multiresolution Algorithm for Rotation-Invariant Matching of Planar Shapes,
PRL(13), September 1992, pp. 669-676. BibRef 9209

Ciuti, V., Marola, G., Santerini, D.,
An Algorithm for the Localization of Rotated and Scaled Objects,
PRL(11), 1990, pp. 59-66. BibRef 9000

Lei, G.,
Recognition of Planar Objects in 3-D Space from Single Perspective Views Using Cross Ratio,
RA(6), 1990, pp. 432-437. BibRef 9000

Weinshall, D.[Daphna],
Minimal Decomposition of Model-Based Invariants,
JMIV(10), No. 1, January 1999, pp. 75-85.
DOI Link BibRef 9901

Lourakis, M.I.A., Halkidis, S.T., Orphanoudakis, S.C.,
Matching Disparate Views of Planar Surfaces Using Projective Invariants,
IVC(18), No. 9, June 2000, pp. 673-683.
WWW Link. 0004
BibRef
Earlier: BMVC98(I: 94-104).
PS File. BibRef

Adán, A.[Antonio], Cerrada, C.[Carlos], Feliu, V.[Vicente],
Global shape invariants: a solution for 3D free-form object discrimination/identification problem,
PR(34), No. 7, July 2001, pp. 1331-1348.
WWW Link. 0105
See also Active object recognition based on Fourier descriptors clustering. BibRef

Tien, S.C.[Shen-Chi], Chia, T.L.[Tsorng-Lin], Lu, Y.B.[Yi-Bin],
Using cross-ratios to model curve data for aircraft recognition,
PRL(24), No. 12, August 2003, pp. 2047-2060.
WWW Link. 0304
BibRef

Dibos, F.[Françoise], Frosini, P.[Patrizio], Pasquignon, D.[Denis],
The Use of Size Functions for Comparison of Shapes Through Differential Invariants,
JMIV(21), No. 2, September 2004, pp. 107-118.
DOI Link 0409
Use size to reduce errors in invariants. BibRef


Chuang, J.H.[Jen-Hui], Kao, J.H.[Jau-Hong], Lin, H.H.[Horng-Horng], Chiu, Y.T.[Yu-Ting],
Practical Error Analysis of Cross-Ratio-Based Planar Localization,
PSIVT07(727-736).
Springer DOI 0712
BibRef

Huynh, D.,
The Cross Ratio: A Revisit to its Probability Density Function,
BMVC00(xx-yy).
PDF File. 0009
BibRef

Wang, G.Y.[Guo-Yu], Houkes, Z., Regtien, P.P.L., Korsten, M.J., Ji, G.,
A Statistical Model to Describe Invariants Extracted from a 3-D Quadric Surface Patch and its Applications in Region-Based Recognition,
ICPR98(Vol I: 668-672).
IEEE DOI 9808
BibRef

Simon, D.A.[David A.], Kanade, T.[Takeo],
Geometric Constraint Analysis and Synthesis: Methods for Improving Shape-Based Registration Accuracy,
DARPA97(901-910). BibRef 9700

Muresan, L.[Lucian],
2D-2D geometric transformation invariant to arbitrary translations, rotations and scales,
CAIP97(90-97).
Springer DOI 9709
BibRef

Zribi, M., Fonga, H., Ghorbel, F.,
A Set of Invariant Features for Three-Dimensional Gray Level Objects by Harmonic Analysis,
ICPR96(I: 549-553).
IEEE DOI 9608
(Ecole Nouvelle d'ingenierus, F) BibRef

Lei, Z.B.[Zhi-Bin], Tasdizen, T.[Tolga], and Cooper, D.B.[David B.],
PIMs and Invariant Parts for Shape Recognition,
ICCV98(827-832).
IEEE DOI BibRef 9800

Lei, Z.B.[Zhi-Bin], Keren, D., Cooper, D.B.,
Computationally fast Bayesian recognition of complex objects based on mutual algebraic invariants,
ICIP95(II: 635-638).
IEEE DOI 9510
BibRef

Cooper, D.B.[David B.], Lei, Z.B.[Zhi-Bin],
On representation and invariant recognition of complex objects based on patches and parts,
ORCV94(139-153).
Springer DOI 9412
BibRef

Sanfeliu, A., Llorens, A., Emde, W.,
Sensibility, Relative Error and Error Probability of Projective Invariants of Planar Surfaces of 3D Objects,
ICPR92(I:328-331).
IEEE DOI BibRef 9200

Yu, X., Bui, T.D., and Krzyzak, A.,
Invariants and Pose Determination,
VF91(623-632). Based on matching surface patches. BibRef 9100

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Invariants, Projective, Perspective .


Last update:Sep 25, 2017 at 16:36:46