11.2.4 Region Techniques for Range and Surfaces

Hoover, A., Jean-Baptiste, G., Jiang, X.Y., Flynn, P.J., Bunke, H., Goldgof, D.B., Bowyer, K.W., Eggert, D.W., Fitzgibbon, A.W., Fisher, R.B.,
An Experimental Comparison of Range Image Segmentation Algorithms,
PAMI(18), No. 7, July 1996, pp. 673-689.
IEEE Abstract.
WWW Version. 9608
BibRef Edinburgh Evaluation, Range Segmentation. Comparisons. Algorithm UB: ( See also Fast Segmentation of Range Images by Scan Line Grouping. ). Algorithm WSU: ( See also BONSAI: 3D Object Recognition Using Constrained Search. and See also Segmentation and Classification of Range Images. ). Algorithm USF: ( See also Curvature-Based Approach to Terrain Recognition, A. ). Algorithm UE: No exact reference given, but try: ( See also High-Level CAD Model Acquisition from Range Images. or See also Experiments in Curvature-Based Segmentation of Range Data. ). Depending on the measure (over/under segmentation, etc.) different ones ranked best. Read this paper first. BibRef

Hoover, A., Jean-Baptiste, G., Goldgof, D., Bowyer, K.W., Flynn, P.J., Jiang, X.Y., Bunke, H.,
Range Image Segmentation: The User's Dilemma,
SCV95(323-328).
IEEE Top Reference. Evaluation, Range Segmentation. Univ. of South Florida. Washington State Univ. Univ. of Bern. Evaluation of segmentation of range into planar patches. BibRef 9500

Jiang, X.Y., Hoover, A., Jean-Baptiste, G., Goldgof, D.B., Bowyer, K.W., Bunke, H.,
A Methodology for Evaluating Edge Detection Techniques for Range Images,
ACCV95(415-419). Hand ground truth data for evaluation. BibRef 9500

Hoover, A., Jean-Baptiste, G., Goldgof, D.B., and Bowyer, K.W.,
A Methodology for Evaluating Range Image Segmentation Techniques,
WACV94(264-271).
IEEE Abstract. BibRef 9400

Jiang, X., Bowyer, K.W., Morioka, Y., Hiura, S., Sato, K., Inokuchi, S., Bock, M., Guerra, C., Loke, R.E., du Buf, J.M.H.,
Some Further Results of Experimental Comparison of Range Image Segmentation Algorithms,
ICPR00(Vol IV: 877-881).
IEEE DOI Link
HTML Version. 0009
BibRef

Hoffman, R.L.[Richard L.], and Jain, A.K.,
Segmentation and Classification of Range Images,
PAMI(9), No. 5, September 1987, pp. 608-620. BibRef 8709
Earlier: CVPR86(446-451). BibRef
And: MSU-ENGR-86-002, Michigan State Univ, E. Lansing, MI. See the comparison: See also Experimental Comparison of Range Image Segmentation Algorithms, An. Segment the range image into surface patches, classify the patch, merge compatible patches to produce faces. BibRef

Hoffman, R.L.[Richard L.],
Object Recognition from Range Images,
Ph.D.Department of Computer Science, Michigan State University, 1986. BibRef 8600

Dubes, R.C., Jain, A.K., Nadabar, S.G., and Chen, C.C.,
MRF Model-Based Algorithms for Image Segmentation,
ICPR90(I: 808-814).
IEEE DOI Link Two level segmentation process. Neighborhoods. See also Random Field Models in Image Analysis. BibRef 9000

Jain, A.K., and Nadabar, S.G.,
MRF Model-Based Segmentation of Range Images,
ICCV90(667-671).
IEEE DOI Link BibRef 9000

Hoffman, R., and Jain, A.K.,
Evidence-Based Recognition of 3-D Objects,
PAMI(10), No. 6, November 1988, pp. 783-802.
IEEE Abstract.
WWW Version. BibRef 8811
Earlier:
An Evidence-Based 3D Vision System for Range Images,
ICCV87(521-525). BibRef
Earlier: MSU-ENGR-86-013, Comp Sci Dept Report, 1986. Recognize Range Data. Builds on the description program to recognize the objects. Find notable features, use a rulebase with conditions and weights to compute similarity measure between scene and object. BibRef

Hoffman, R., and Jain, A.K.,
Learning Rules for 3-D Object Recognition,
CVPR88(885-892).
IEEE Abstract. Rules for the other papers. BibRef 8800

Yokoya, N., and Levine, M.D.,
Volumetric Shapes of Solids of Revolution from a Single-View Range Image,
CVGIP(59), No. 1, January 1994, pp. 43-52.
WWW Version. BibRef 9401
Earlier:
Volumetric description of solids of revolution in a range image,
ICPR90(I: 303-308).
IEEE DOI Link 9006
BibRef

Yokoya, N., and Levine, M.D.,
Range Image Segmentation Based on Differential Geometry: A Hybrid Approach,
PAMI(11), No. 6, June 1989, pp. 643-649.
IEEE Abstract.
WWW Version. BibRef 8906
Earlier:
A Hybrid Approach to Range Image Segmentation,
ICPR88(I: 1-5).
IEEE DOI Link 8811
Generate surfaces (from range data) using both edge and region information. BibRef

Henderson, T.C.,
Efficient 3-D Object Representations for Industrial Vision Systems,
PAMI(5), No. 6, November 1983, pp. 609-618. BibRef 8311
Earlier:
Efficient Segmentation Method for Range Date,
SPIE(336), Robot Vision, May 1982, pp. 46-47. Region growing to describe 3-D objects by surface patches. BibRef

Liou, S.P., and Jain, R.C.,
An Approach to Three-Dimensional Image Segmentation,
CVGIP(53), No. 3, May 1991, pp. 237-252.
WWW Version. BibRef 9105
Earlier:
A Parallel Technique for Three-Dimensional Image Segmentation,
ICPR90(I: 201-203).
IEEE DOI Link Segmentation is based on 3-D edge detection. This segmentation is applied to spatio-temporal image sequences (slices, or time based). See also Parallel Technique for Signal-Level Perceptual Organization, A. BibRef

Thonnat, M.,
Semantic Interpretation of 3-D Stereo Data: Finding the Main Structures,
PRAI(2), 1988, pp. 509-525. BibRef 8800
Earlier: ICPR86(1051-1054). BibRef

Ma, R., Thonnat, M.,
Object Detection in Outdoor Scenes by Disparity Map Segmentation,
ICPR92(I:546-549).
IEEE DOI Link BibRef 9200

Han, J.H., and Volz, R.A.,
Region Grouping from a Range Image,
CVPR88(241-248).
IEEE Abstract. Similar to Fan's work. Find convex surface regions and look at the boundaries between them. BibRef 8800

Abdelmalek, N.N.,
Algebraic Error Analysis for Surface Curvatures and Segmentation of 3-D Range Images,
PR(23), No. 8, 1990, pp. 807-817.
WWW Version. BibRef 9000
Earlier:
Algebraic error analysis for surface curvatures of 3-D range images obtained by different methods,
ICPR90(I: 529-534).
IEEE DOI Link 9006
BibRef

Bhandarkar, S.M., Siebert, A.,
Integrating Edge and Surface Information for Range Image Segmentation,
PR(25), No. 9, September 1992, pp. 947-962.
WWW Version. BibRef 9209

Parvin, B., and Medioni, G.G.[Gerard G.],
B-rep Object Description from Multiple Range Views,
IJCV(20), No. 1/2, 1996, pp. 81-112. BibRef 9600
Earlier:
B-reps from Unregistered Multiple Range Image,
CRA92(1602-1607). BibRef
Earlier:
Segmentation of Range Images into Planar Surfaces by Split and Merge,
CVPR86(415-417). Use surface normals as the feature for the splitting. An unrelated, though similar, paper is under edge detection in range data. See also Adaptive Multiscale Feature Extraction from Range Data. BibRef

Strasters, K.C., and Gerbrands, J.J.,
Three-Dimensional Image Segmentation Using a Split, Merge and Group Approach,
PRL(12), No. 5, May 1991, pp. 307-325. BibRef 9105

Strasters, K.C., Smeulders, A.W.M., vander Voort, H.T.M.,
3-D Texture Characterized by Accessibility Measurements, Based on the Grey Weighted Distance Transform,
BioImaging(2), No. 1, 1994, pp. 1-21. BibRef 9400

Hoover, A., Goldgof, D., Bowyer, K.W.,
Extracting a Valid Boundary Representation from a Segmented Range Image,
PAMI(17), No. 9, September 1995, pp. 920-924.
IEEE Abstract.
WWW Version. Line Labels. BibRef 9509

Hoover, A., Goldgof, D., Bowyer, K.W.,
Dynamic-Scale Model Construction From Range Imagery,
PAMI(20), No. 12, December 1998, pp. 1352-1357.
IEEE Abstract.
WWW Version. BibRef 9812

Bose, S.K., Biswas, K.K., Gupta, S.K.,
An Integrated Approach for Range Image Segmentation and Representation,
AIEng(10), No. 3, August 1996, pp. 243-252. 9606
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Chatterji, G.B., Sridhar, B.,
Discrete Range Clustering Using Monte-Carlo Methods,
SMC-A(26), No. 6, November 1996, pp. 832-837.
IEEE Top Reference. 9611
BibRef

Baccar, M., Gee, L.A., Gonzalez, R.C., Abidi, M.A.,
Segmentation of Range Images Via Data Fusion and Morphological Watersheds,
PR(29), No. 10, October 1996, pp. 1673-1687.
WWW Version. Data Fusion. BibRef 9610

Umasuthan, M., Wallace, A.M.,
Outlier Removal and Discontinuity Preserving Smoothing of Range Data,
VISP(143), No. 3, June 1996, pp. 191-200. 9608
Smoothing of range data for segmentattion. BibRef

Wu, M.C., Liu, C.R.,
Analysis of Machined Feature Recognition Techniques Based on B-Rep,
CAD(28), No. 8, August 1996, pp. 603-616. 9607
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Rimey, R.D., Cohen, F.S.,
A Maximum-Likelihood Approach to Segmenting Range Data,
RA(4), 1988, pp. 277-286. BibRef 8800

Zhao, D.M., Zhang, X.T.,
Range-Data-Based Object Surface Segmentation via Edges and Critical-Points,
IP(6), No. 6, June 1997, pp. 826-830.
IEEE DOI Link 9705
BibRef

Chen, Y.H., Liu, C.Y.,
Robust Segmentation of CMM Data Based on NURBS,
IJAMT(13), No. 8, 1997, pp. 530-534. 9712
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Baccar, M., Gee, L.A., Abidi, M.A.,
Reliable Location and Regression Estimates with Application to Range Image Segmentation,
JMIV(11), No. 3, December 1999, pp. 195-205.
WWW Version. BibRef 9912

Köster, K.[Klaus], Spann, M.[Michael],
MIR: An Approach to Robust Clustering-Application to Range Image Segmentation,
PAMI(22), No. 5, May 2000, pp. 430-444.
IEEE Abstract.
WWW Version. 0008
Segmentation, Region Growing. With comparison of results using the method of See also Experimental Comparison of Range Image Segmentation Algorithms, An. (And the same 4 algorithms used there.) Works better for most cases. Use the Mutual Inlier Ratio (MIR) to compare regions for a region growing algorithm. This reduces impact of outlier points merged into regions. BibRef

Benlamri, R.,
Range image segmentation of scenes with occluded curved objects,
PRL(21), No. 12, November 2000, pp. 1051-1060. 0011
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Benlamri, R.[Rachid], Al-Marzooqi, Y.[Yousuf],
Free-form object segmentation and representation from registered range and color images,
IVC(22), No. 9, 20 August 2004, pp. 703-717.
WWW Version. 0407
BibRef

Werghi, N., Fisher, R.B., Robertson, C., Ashbrook, A.P.,
Faithful recovering of quadric surfaces from 3D range data by global fitting,
ShapeMod(6), No. 1, 2000, pp. 65-78. BibRef 0001 Edinburgh 0001
BibRef
Earlier: A1, A2, A4, A3:
Faithful recovering of quadric surfaces from 3D range data,
3DIM99(280-289).
IEEE DOI Link BibRef
Earlier: A4, A2, A3, A1:
Finding Surface Correspondence for Object Recognition and Registration Using Pairwise Geometric Histograms,
ECCV98(II: 674).
WWW Version. BibRef Edinburgh BibRef

Robertson, C.[Craig], Fisher, R.B.[Robert B.],
Parallel Evolutionary Registration of Range Data,
CVIU(87), No. 1-3, July 2002, pp. 39-50.
WWW Version. 0301
BibRef Edinburgh BibRef

Ashbrook, A.P., Fisher, R.B., Robertson, C., Werghi, N.,
Construction of Articulated Models from Range Data,
BMVC99(183-192),
PDF Version. BibRef 9900 Edinburgh BibRef
Earlier:
Segmentation of Range Data into Rigid Subsets Using Surface Patches,
ICCV98(201-206).
IEEE DOI Link BibRef
Earlier:
Segmentation of Range Data into Rigid Subsets using Planar Surface Patches,
BMVC97(530-539). BibRef Edinburghrange data segmentation
HTML Version. Or
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Ashbrook, A.P., Fisher, R.B.,
Segmentation of Range Data for the Automatic Construction of Models of Articulated Objects,
Non-Rigid97(xx-yy). BibRef 9700 Edinburgh BibRef

Revol-Muller, C.[Chantal], Peyrin, F.[Francoise], Carrillon, Y.[Yannick], Odet, C.[Christophe],
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PRL(23), No. 1-3, January 2002, pp. 137-150.
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Revol-Muller, C.,
Automated 3d Region Growing Algorithm Governed by an Evaluation Function,
ICIP00(Vol III: 440-443).
IEEE Abstract. 0008
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Bellon, O.R.P.[Olga Regina Pereira], Silva, L.[Luciano],
New improvements to range image segmentation by edge detection,
SPLetters(9), No. 2, February 2002, pp. 43-45.
IEEE Top Reference. 0204
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Bellon, O.R.P.[Olga Regina Pereira], Direne, A.I.[Alexandre Ibrahim], Silva, L.[Luciano],
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Silva, L., Bellon, O.R.P., Gotardo, P.F.U.,
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Edge-based Image Segmentation Using Curvature Sign Maps from Reflectance and Range Images,
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IEEE Abstract. 0108
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Gotardo, P.F.U., Bellon, O.R.P., Silva, L.,
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IEEE Abstract. 0307
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Bellon, O.R.P.[Olga R. P.], Tozzi, C.L.[Clesio L.],
An integrated approach for segmentation and representation of range images,
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Planar background elimination in range images: A practical approach,
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WWW Version. 0301
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Khalifa, I.[Inas], Moussa, M.[Medhat], Kamel, M.[Mohamed],
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Khalifa, I.,
Range Image Segmentation with Application to CAD Model Acquisition,
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Properties of a Three-Dimensional Island Hierarchy for Segmentation of 3D Images with the Color Structure Code,
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A graph-spectral approach to correspondence matching,
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Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
The Facet Model for Descriptions .