8.7 Combining Region and Edge Based Techniques

Stronghill, J.P.[James P.], and Rosenfeld, A.,
A Region Coloring Technique for Scene Analysis,
CACM(16), No. 4, April 1973, pp. 237-246. Segmentation, Region Growing. Segmentation, Texture. Grow regions up to edges. Does a texture based segmentation. BibRef 7304

Nakagawa, Y., and Rosenfeld, A.,
Edge/Border Coincidence as an Aid in Edge Extraction,
SMC(8), 1978, pp. 899-901. BibRef 7800

Broder, A., and Rosenfeld, A.,
Gradient Magnitude as an Aid in Color Pixel Classification,
SMC(11), 1981, pp. 248-249. BibRef 8100

Milgram, D.L., and Herman, H.,
Clustering Edge Values for Threshold Selection,
CGIP(10), No. 3, July 1979, pp. 272-280.
WWW Version. Segmentation, 2-D Histogram. Segmentation, Histogram. Compute histograms of edge information in addition to the intensity (2-D histograms). BibRef 7907

Milgram, D.L.,
Constructing Trees for Region Description,
CGIP(11), No. 1, September 1979, pp. 88-99.
WWW Version. BibRef 7909

Milgram, D.L.,
Region Extraction Using Convergent Evidence,
CGIP(11), No. 1, September 1979, pp. 1-12.
WWW Version. BibRef 7909
And: DARPA77(58-64). BibRef
And:
Progress Report of Segmentation Using Convergent Evidence,
DARPAO77(104-108). Segmentation, 2-D Histogram. Segmentation, Histogram. BibRef

Milgram, D.L.,
Segmentation Using Convergent Evidence,
PRAI-78(73-77). BibRef 7800

Milgram, D.L.,
Edge Point Linking Using Convergent Evidence,
DARPAN78(85-91). BibRef 7800

Milgram, D.L., and Rosenfeld, A., Tisdale, W.,
Final Report,
UMDMarch 31, 1978. Superslice. Segmentation. Image models, preprocessing - median filter - histogram transforming - edge detection; threshold selection; noise cleaning and labeling; superslice and hyperslice recursive region segmentation; feature extraction; reg. classify result; dynamic environment; hardware. BibRef 7803

Kawai, S.,
A Boundary Curve Criterion,
CGIP(11), No. 3, November 1979, pp. 281-289.
WWW Version. BibRef 7911

Lemkin, P.,
The Boundary Trace Transform: An Edge and Region Enhancement Transform,
CGIP(9), No. 2, February 1979, pp. 150-165.
WWW Version. BibRef 7902

Perkins, W.A.,
Area Segmentation of Images Using Edge Points,
PAMI(2), No. 1, January 1980, pp. 8-15. BibRef 8001
Earlier:
Region Segmentation of Images by Expansion and Contraction of Edge Points,
IJCAI79(699-701). Generate thinned edge image; expand each edge pixel; extract the remaining regions - delete small regions; shrink the edges back and add these pixels to the regions; eliminate small edge regions; problem when gaps are large. BibRef

Lattuati, V., Lemoine, D.,
Closed Contour Extraction Applied to Meteorological Pictures,
PR(15), No. 3, 1982, pp. 145-152.
WWW Version. 0309
BibRef

Zamperoni, P.,
Contour Tracing of Grey-Scale Images Based on 2-D Histograms,
PR(15), No. 3, 1982, pp. 161-165.
WWW Version. BibRef 8200

Lavin, P.,
Restoration of a Feature Closed Class of Two-Dimensional Images,
PAMI(5), No. 1, January 1983, pp. 14-24. BibRef 8301

O'Gorman, L., and Sanderson, A.C.,
The Wedge Filter Technique for Convex Boundary Estimation,
PAMI(7), No. 3, May 1985, pp. 326-332. BibRef 8505

Stansfield, S.A.[Sharon A.],
ANGY: A Rule-Based Expert System for Automatic Segmentation of Coronary Vessels from Digital Subtracted Angiograms,
PAMI(8), No. 3, March 1986, pp. 188-199. BibRef 8603
Earlier:
Angy: A Rule-Based Expert System for Identifying and Isolating Coronary Vessels in Digital Angiograms,
CAIA84(606-609). Segmentation, Rule-Based. Combines edge (simplified Canny) and a bad region segmentation (thresholds in squares which are then merged), using an OPS5 rule-based system. Lots of problems, probably more interesting from the OPS5 viewpoint than the segmentation viewpoint. BibRef

Nair, H.,
Reconstruction of Planar Boundaries from Incomplete Information,
CVGIP(39), No. 3, September 1987, pp. 383-387.
WWW Version. BibRef 8709

Liou, S.P., Chiu, A.H., and Jain, R.C.,
A Parallel Technique for Signal-Level Perceptual Organization,
PAMI(13), No. 4, April 1991, pp. 317-325.
IEEE Abstract.
WWW Version. Perceptual Grouping. Segmentation, Region Growing. Single parallel step, not iterative. Uses what they call signal-level perceptual organization involving partioning and identification of regions. Combine the results of edge detection and region growing ideas. They apply this to range and intensity data. See also Approach to Three-Dimensional Image Segmentation, An. BibRef 9104

Haddon, J.F., and Boyce, J.F.,
Image Segmentation by Unifying Region and Boundary Information,
PAMI(12), No. 10, October 1990, pp. 929-948.
IEEE Abstract.
WWW Version. Segmentation, Texture. Using cooccurrence matrices, classify a pixel into interior or boundary points. Minimize the entropy of the local information based on conditional probabilities. This generates homogeneous regions and an edge map. The paper has a good set of references for texture based image segmentation. BibRef 9010

Haddon, J.F., Boyce, J.F.,
Texture Segmentation and Region Classification by Orthogonal Decomposition of Cooccurrence Matrices,
ICPR92(I:692-695).
IEEE DOI Link BibRef 9200

Haddon, J.F.[John F.],
Generalised threshold selection for edge detection,
PR(21), No. 3, 1988, pp. 195-203.
WWW Version. 0309
BibRef

Dudani, S.A.,
Region Extraction Using Boundary Following,
PRAI-76(216-232). BibRef 7600

Hertz, L.[Lois], and Schafer, R.W.[Ronald W.],
Multilevel Thresholding Using Edge Matching,
CVGIP(44), No. 3, December 1988, pp. 279-295.
WWW Version. Find varying thresholds by keying on edge locations. BibRef 8812

Hertz, L., Schafer, R.W.,
Measurement of Edge Coincidence in Image Thresholdings,
JVCIR(4), 1993, pp. 149-156. BibRef 9300

Hertz, L., Schafer, R.W.,
Postprocessing of Thresholded Images to Maximize Edge Coincidence,
JVCIR(6), No. 2, June 1995, pp. 178-188. BibRef 9506

Colchester, A.C.F., Ritchings, R.T., Kodikara, N.D.,
Image Segmentation Using Maximum Gradient Profiles Orthogonal to Edges,
IVC(8), No. 3, August 1990, pp. 211-217.
WWW Version. BibRef 9008

Griffin, L.D.[Lewis D.], Colchester, A.C.F.[Alan C.F.],
Superficial and deep structure in linear diffusion scale space: isophotes, critical points and separatrices,
IVC(13), No. 7, September 1995, pp. 543-557.
WWW Version. 0401
BibRef
Earlier: Roell, S.A.[Stefan A.], Studholme, C.[Colin],
Hierarchical Segmentation Satisfying Constraints,
BMVC94(135-144).
PDF Version. See also Feature-Based Image Analysis. BibRef

Griffin, L.D., Robinson, G.P., Colchester, A.C.F.,
Multiscale Hierarchical segmentation,
BMVC93(xx).
PDF Version. 9309
(Guys Hospital). BibRef

Colchester, A.C.F., Robinson, G.P., Griffin, L.D.,
A unified approach to the segmentation of grey-level and dot-pattern images,
ICPR92(III:319-322).
IEEE DOI Link 9208
BibRef

Griffin, L.D., Colchester, A.C.F., Robinson, G.P.,
Scale and Segmentation of Grey-Level Images Using Maximum Gradient Paths,
IVC(10), No. 6, July-August 1992, pp. 389-402.
WWW Version. BibRef 9207

Chu, C.C., and Aggarwal, J.K.,
The Integration of Image Segmentation Maps Using Region and Edge Information,
PAMI(15), No. 12, December 1993, pp. 1241-1252.
IEEE Abstract.
WWW Version. BibRef 9312
Earlier:
The Integration of Region and Edge-Based Segmentation,
ICCV90(117-120).
IEEE DOI Link BibRef

Mital, D.P., Teoh, E.K., Lim, A.W.T.,
A Hybrid Method Towards the Segmentation of Range Images for 3-D Object Recognition,
PRAI(8), 1994, pp. 969-995. BibRef 9400

Lim, A.W.T., Teoh, E.K., Mital, D.P.,
A Hybrid Method for Range Image Segmentation,
JMIV(4), 1994, pp. 69-80. BibRef 9400

Kaveti, S., Teoh, E.K.[Eam Khwang], Wang, H.[Han],
Second-Order Implicit Polynomials for Segmentation of Range Images,
PR(29), No. 6, June 1996, pp. 937-949.
WWW Version. 9606
BibRef
Earlier:
Robust representation and recognition of free-form objects,
ICIP96(III: 587-590).
IEEE DOI Link 9610
BibRef

Le Moigne, J., Tilton, J.C.,
Refining Image Segmentation by Integration of Edge and Region Data,
GeoRS(33), No. 3, May 1995, pp. 605-615.
IEEE Top Reference. BibRef 9505

Lerner, B.T.[Bao T.], Campbell, W.J.[William J.], and Le Moigne, J.[Jacqueline],
Image Segmentation by Integration of Edge and Region Data: The Influence of Edge Detection Algorithms,
ARPA94(II:1541-1545). BibRef 9400

Gambotto, J.P.,
A New Approach to Combining Region Growing and Edge Detection,
PRL(14), 1993, pp. 869-875. BibRef 9300

Jumarie, G.,
Contour Detection by Using Information Theory of Deterministic Functions,
PRL(12), 1991, pp. 25-29. BibRef 9100

Gamba, P., Lodola, R., Mecocci, A.,
Scene Interpretation by Fusion of Segment and Region Information,
IVC(15), No. 7, July 1997, pp. 499-509.
WWW Version. 9708
BibRef

Cho, K.J., Meer, P.,
Image Segmentation from Consensus Information,
CVIU(68), No. 1, October 1997, pp. 72-89.
WWW Version.
HTML Version. 9710
BibRef

Chakraborty, A.[Amit], Duncan, J.S.[James S.],
Game-Theoretic Integration for Image Segmentation,
PAMI(21), No. 1, January 1999, pp. 12-30.
IEEE Abstract.
WWW Version. Integrate edge based and region based approaches. Applied to MR images, with their kinds of noise. BibRef 9901

Izquierdo, E., Ghanbari, M.,
Nonlinear Gaussian filtering approach for object segmentation,
VISP(146), No. 3, 1999, pp. 137. BibRef 9900

Ma, W.Y., Manjunath, B.S.,
EdgeFlow: A Technique for Boundary Detection and Image Segmentation,
IP(9), No. 8, August 2000, pp. 1375-1388.
IEEE DOI Link 0008
BibRef
Earlier:
Edge Flow: A Framework of Boundary Detection and Image Segmentation,
CVPR97(744-749).
IEEE Abstract.
WWW Version. 9704

PDF Version. Color and texture; EF: "flow" like description of edge values. BibRef

Iannizzotto, G., Vita, L.,
Fast and Accurate Edge-Based Segmentation with No Contour Smoothing in 2-D Real Images,
IP(9), No. 7, July 2000, pp. 1232-1237.
IEEE DOI Link 0006
BibRef
Earlier:
A fast, accurate method to segment and retrieve object contours in real images,
ICIP96(I: 841-843).
IEEE DOI Link 9610
BibRef

Chung, D.H., Sapiro, G.,
On the Level Lines and Geometry of Vector-Valued Images,
SPLetters(7), No. 9, September 2000, pp. 241-243.
IEEE Top Reference. 0008
BibRef

Ida, T.[Takashi], Sambonsugi, Y.[Yoko],
Self-Affine Mapping System and Its Application to Object Contour Extraction,
IP(9), No. 11, November 2000, pp. 1926-1936.
IEEE DOI Link 0011
BibRef
Earlier:
Self-affine Mapping System for Object Contour Extraction,
ICIP99(III:250-254).
IEEE Abstract. BibRef

Chang, C.Y.[Chuan-Yu], Chung, P.C.[Pau-Choo],
Medical image segmentation using a contextual-constraint-based Hopfield neural cube,
IVC(19), No. 9-10, August 2001, pp. 669-678.
WWW Version. 0108
BibRef

Sang, N.[Nong], Zhang, T.X.[Tian-Xu],
Segmentation of FLIR images by Hopfield neural network with edge constraint,
PR(34), No. 4, April 2001, pp. 811-821.
WWW Version. 0101
BibRef

Yan, C.X.[Cheng-Xin], Sang, N.[Nong], Zhang, T.X.[Tian-Xu],
Local entropy-based transition region extraction and thresholding,
PRL(24), No. 16, December 2003, pp. 2935-2941.
WWW Version. 0310
BibRef

Tang, Q.L.[Qi-Ling], Sang, N.[Nong], Zhang, T.X.[Tian-Xu],
Contour detection based on contextual influences,
IVC(25), No. 8, 1 August 2007, pp. 1282-1290.
WWW Version. 0706
Contour detection; Contextual influences; Visual mechanisms; Suppression; Enhancement BibRef

Bhalerao, A.H., Wilson, R.,
Unsupervised image segmentation combining region and boundary estimation,
IVC(19), No. 6, April 2001, pp. 353-368.
WWW Version. 0105
BibRef

Bhalerao, A.H., Wilson, R.,
Affine Invariant Image Segmentation,
BMVC04(xx-yy).
HTML Version. 0508
BibRef

Brejl, M., Sonka, M.,
Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples,
MedImg(19), No. 10, October 2000, pp. 973-985.
IEEE Top Reference. 0110
BibRef

Shiffman, S., Rubin, G.D., Napel, S.,
Medical image segmentation using analysis of isolable-contour maps,
MedImg(19), No. 11, November 2000, pp. 1064-1074.
IEEE Top Reference. 0110
BibRef

Kermad, C.D.[Chafik Djalal], Chehdi, K.[Kacem],
Automatic image segmentation system through iterative edge-region co-operation,
IVC(20), No. 8, June 2002, pp. 541-555.
WWW Version. 0206
BibRef

Muñoz, X., Freixenet, J., Cufí, X., Martí, J.,
Strategies for image segmentation combining region and boundary information,
PRL(24), No. 1-3, January 2003, pp. 375-392.
HTML Version. 0211
BibRef
Earlier: A1, A4, A3, A2:
Unsupervised active regions for multiresolution image segmentation,
ICPR02(II: 905-908).
IEEE DOI Link 0211
BibRef

Bosch, A., Munoz, X., Freixenet, J.,
Segmentation and description of natural outdoor scenes,
IVC(25), No. 5, 1 May 2007, pp. 727-740.
WWW Version. 0703
Image understanding; Object classification; Object segmentation BibRef

Freixenet, J.[Jordi], Muñoz, X.[Xavier], Martí, J.[Joan], Lladó, X.[Xavier],
Colour Texture Segmentation by Region-Boundary Cooperation,
ECCV04(Vol II: 250-261).
WWW Version. 0405
BibRef

Muñoz, X., Freixenet, J., Cufí, X., Martí, J.,
Active regions for colour texture segmentation integrating region and boundary information,
ICIP03(III: 453-456).
IEEE Abstract. 0312
BibRef
Earlier: A1, A2, A4, A3:
Active Regions for Unsupervised Texture Segmentation Integrating Region and Boundary Information,
Texture02(95-98). 0207
BibRef

Cufí, X., Muñoz, X., Freixenet, J., Martí, J.,
A Concurrent Region Growing Algorithm Guided by Circumscribed Contours,
ICPR00(Vol I: 432-435).
IEEE DOI Link
HTML Version. 0009
BibRef
Earlier: A2, A1, A3, A4:
A New Approach to Segmentation Based on Fusing Circumscribed Contours, Region Growing and Clustering,
ICIP00(Vol I: 800-803).
IEEE Abstract. 0008
BibRef

Martin, D.R.[David R.], Fowlkes, C.C.[Charless C.], Malik, J.,
Learning to detect natural image boundaries using local brightness, color, and texture cues,
PAMI(26), No. 5, May 2004, pp. 530-549.
IEEE Abstract. 0404
Detect and localize boundaries using local measurements. BibRef

Arbelaez, P.[Pablo], Fowlkes, C.C.[Charless C.], and Martin, D.R.[David R.],
The Berkeley Segmentation Dataset and Benchmark,
Online2007. Dataset, Segmentation. Code, Segmentation.
WWW Version. The updated code and data for the earlier paper. See also Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics, A. BibRef 0700

Martin, D.R.[David R.], Fowlkes, C.C.[Charless C.], Tal, D.[Doron], Malik, J.[Jitendra],
A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics,
ICCV01(II: 416-423).
IEEE DOI Link 0106
BibRef
And:
A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms,
PercOrg01(xx-yy). Multiple human segmentations and a segmentation consistency measure. Human-human are consistent with the measure, different images are not consistent. Promised online availability. 1000 images with hand segmentations. Multiple hand segmentations. BibRef

Martin, D.R.[David R.],
An Empirical Approach to Grouping and Segmentation,
Ph.D.dissertation, Univ. of California, Berkeley, 2002. BibRef 0200

Fowlkes, C.C.[Charless C.], Martin, D.R.[David R.], Malik, J.[Jitendra],
Learning Affinity Functions for Image Segmentation: Combining Patch-Based and Gradient-Based Approaches,
CVPR03(II: 54-61).
IEEE Abstract. 0307
BibRef
Earlier:
Understanding Gestalt Cues and Ecological Statistics Using A Database of Human Segmented Images,
PercOrg01(xx-yy). 0106
BibRef

Wagman, A.[Adam], Bachelder, I.A.[Ivan A.],
Method for finding contours in an image of an object,
US_Patent6,941,016, Sep 6, 2005
WWW Version. BibRef 0509

Ma, L.[Lei], Zhang, X.P.[Xiao-Ping], Si, J., Abousleman, G.P.,
Bidirectional Labeling and Registration Scheme for Grayscale Image Segmentation,
IP(14), No. 12, December 2005, pp. 2073-2081.
IEEE DOI Link 0512
BibRef
Earlier:
Bi-directional gradient labeling and registration for gray-scale image segmentation,
ICIP03(I: 365-368).
IEEE Abstract. 0312
BibRef

Prasad, L.[Lakshman], Skourikhine, A.N.[Alexei N.],
Vectorized image segmentation via trixel agglomeration,
PR(39), No. 4, April 2006, pp. 501-514.
WWW Version. 0604
Delaunay triangulation; Vectorization; Perceptual grouping; Polygonal decomposition; Region and boundary duality BibRef

Tu, Z.W.[Zhuo-Wen], Zhu, S.C.[Song-Chun],
Parsing Images into Regions, Curves, and Curve Groups,
IJCV(69), No. 2, August 2006, pp. 223-249.
Springer DOI Link 0606
BibRef
Earlier:
Parsing Images into Region and Curve Processes,
ECCV02(III: 393 ff.).
HTML Version. 0205
Layered representation with both region and curve models. BibRef

Pednekar, A.S.[Amol S.], Kakadiaris, I.A.[Ioannis A.],
Image Segmentation Based on Fuzzy Connectedness Using Dynamic Weights,
IP(15), No. 6, June 2006, pp. 1555-1562.
IEEE DOI Link 0606
Capture both homogenity and nearness to value. BibRef

Chittajallu, D.R., Brunner, G., Kurkure, U., Yalamanchili, R.P., Kakadiaris, I.A.,
Fuzzy-Cuts: A knowledge-driven graph-based method for medical image segmentation,
CVPR09(715-722).
IEEE DOI Link 0906
BibRef

Roh, M.C.[Myung-Cheol], Kim, T.Y.[Tae-Yong], Park, J.[Jihun], Lee, S.W.[Seong-Whan],
Accurate object contour tracking based on boundary edge selection,
PR(40), No. 3, March 2007, pp. 931-943.
WWW Version. 0611
Object contour tracking; Boundary edge selection; Optical flow; Contour normal direction; Multi-level edge map BibRef

Park, J.[Jihun], Kim, T.Y.[Tae-Yong], Park, S.[Sunghun],
LOD Canny Edge Based Boundary Edge Selection for Human Body Tracking,
ICIAR04(II: 528-535).
WWW Version. 0409
BibRef

Kim, T.Y.[Tae-Yong], Park, J.[Jihun], Lee, S.W.[Seong-Whan],
Object Boundary Edge Selection for Accurate Contour Tracking Using Multi-level Canny Edges,
ICIAR04(II: 536-543).
WWW Version. 0409
BibRef
And:
Object boundary edge selection using normal direction derivatives of a contour in a complex scene,
ICPR04(IV: 755-758).
IEEE DOI Link 0409
BibRef

Wang, J.H., Chang, F.C., Su, F.W.,
Image segmentation via self-organising fusion,
VISP(153), No. 5, October 2006, pp. 657-665.
WWW Version. 0702
BibRef

Marot, J., Bourennane, S.,
Propagator method for an application to contour estimation,
PRL(28), No. 12, 1 September 2007, pp. 1556-1562.
WWW Version. 0707
Contour estimation; Distorted contours; High resolution methods; Algebra BibRef

He, L.[Lei], Peng, Z.G.[Zhi-Gang], Everding, B.[Bryan], Wang, X.[Xun], Han, C.Y.[Chia Y.], Weiss, K.L.[Kenneth L.], Wee, W.G.[William G.],
A comparative study of deformable contour methods on medical image segmentation,
IVC(26), No. 2, 1 February 2008, pp. 141-163.
WWW Version. 0711
Survey, Snakes. Medical image segmentation; Deformable contour method; Snake; Level set; Comparative study BibRef

Liu, T.W.[Tang-Wei], Zhou, H.Y.[Hui-Yu], Lin, F.Q.[Fa-Quan], Pang, Y.S.[Yu-Sheng], Wu, J.[Ji],
Improving image segmentation by gradient vector flow and mean shift,
PRL(29), No. 1, 1 January 2008, pp. 90-95.
WWW Version. 0711
Segmentation; Gradient vector flow; Mean shift; Snake BibRef

Yang, X.L.[Xu-Lei], Song, Q.[Qing], Wang, Y.[Yue], Cao, A.Z.[Ai-Ze], Wu, Y.L.[Yi-Lei],
A Modified Deterministic Annealing Algorithm for Robust Image Segmentation,
JMIV(30), No. 3, March 2008, pp. 308-324.
Springer DOI Link 0802
BibRef

Gupta, L.[Lalit], Mangai, U.G.[Utthara Gosa], Das, S.[Sukhendu],
Integrating region and edge information for texture segmentation using a modified constraint satisfaction neural network,
IVC(26), No. 8, 1 August 2008, pp. 1106-1117.
WWW Version. 0806
Constraint satisfaction neural networks (CSNN); Segmentation; Texture edge detection; Fuzzy-C means (FCM); Dynamic window BibRef

Awad, M.W., Chehdi, K.[Kacem], Nasri, A.,
Multi-component image segmentation using a hybrid dynamic genetic algorithm and fuzzy C-means,
IET-IPR(3), No. 2, April 2009, pp. 52-62.
WWW Version. 0905
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Awad, M.M.[Mohamad M.], Chehdi, K.[Kacem],
Satellite image segmentation using hybrid variable genetic algorithm,
IJIST(19), No. 3, September 2009, pp. 199-207.
WWW Version. 0909
BibRef

Yuan, J.H.[Jin-Hui], Li, J.[Jianmin], Zhang, B.[Bo],
Scene understanding with discriminative structured prediction,
CVPR08(1-8).
IEEE DOI Link 0806
BibRef

Prasad, M.[Mukta], Zisserman, A.[Andrew], Fitzgibbon, A.W.[Andrew W.], Kumar, M.P.[M. Pawan], Torr, P.H.S.,
Learning Class-Specific Edges for Object Detection and Segmentation,
ICCVGIP06(94-105).
Springer DOI Link 0612
BibRef

Chen, T.[Terrence], Huang, T.S.[Thomas S.],
Boundary correction for total variation regularized L^1 function with applications to image decomposition and segmentation,
ICPR06(II: 316-319).
WWW Version. 0609
BibRef

Dollár, P.[Piotr], Babenko, B.[Boris], Belongie, S.J.[Serge J.], Perona, P.[Pietro], Tu, Z.W.[Zhuo-Wen],
Multiple Component Learning for Object Detection,
ECCV08(II: 211-224).
Springer DOI Link 0810
BibRef

Babenko, B.[Boris], Yang, M.H.[Ming-Hsuan], Belongie, S.J.[Serge J.],
Visual tracking with online Multiple Instance Learning,
CVPR09(983-990).
IEEE DOI Link 0906
BibRef

Babenko, B.[Boris], Dollar, P.[Piotr], Tu, Z.W.[Zhuo-Wen], Belongie, S.J.[Serge J.],
Simultaneous Learning and Alignmennt: Multi-Instance and Multi-Pose Learning,
Faces08(xx-yy). 0810
BibRef

Dollar, P.[Piotr], Tu, Z.W.[Zhuo-Wen], Belongie, S.J.[Serge J.],
Supervised Learning of Edges and Object Boundaries,
CVPR06(II: 1964-1971).
IEEE DOI Link 0606
BibRef

Lange, T.[Tilman], Buhmann, J.M.[Joachim M.],
Regularized Data Fusion Improves Image Segmentation,
DAGM07(234-243).
Springer DOI Link 0709
BibRef

Rabinovich, A.[Andrew], Belongie, S.J.[Serge J.], Lange, T.[Tilman], Buhmann, J.M.[Joachim M.],
Model Order Selection and Cue Combination for Image Segmentation,
CVPR06(I: 1130-1137).
IEEE DOI Link 0606
BibRef

Braun, M.L.[Mikio L.], Lange, T.[Tilman], Buhmann, J.M.[Joachim M.],
Model Selection in Kernel Methods Based on a Spectral Analysis of Label Information,
DAGM06(344-353).
Springer DOI Link 0610
BibRef

Roth, V.[Volker], Lange, T.[Tilman],
Adaptive Feature Selection in Image Segmentation,
DAGM04(9).
WWW Version. 0505
BibRef

Wang, W.[Wei], Chung, R.[Ronald],
Image Segmentation That Optimizes Global Homogeneity in a Variational Framework,
ISVC07(II: 52-61).
Springer DOI Link 0711
BibRef
Earlier:
Image Segmentation That Merges Together Boundary and Region Information,
ACCV06(I:226-235).
Springer DOI Link 0601
BibRef

Wang, W.[Wei], Chung, R.[Ronald],
The Multiplicative Path Toward Prior-Shape Guided Active Contour for Object Detection,
ISVC07(II: 539-548).
Springer DOI Link 0711
BibRef

Wang, W.[Wei], Chung, R.,
Image segmentation via brittle fracture mechanics,
ICIP04(II: 909-912).
IEEE DOI Link 0505
BibRef

Oh, J.T.[Jun-Taek], Kwak, H.W.[Hyun-Wook], Sohn, Y.H.[Young-Ho], Kim, W.H.[Wook-Hyun],
Multi-level Thresholding Using Entropy-Based Weighted FCM Algorithm in Color Image,
ISVC05(437-444).
Springer DOI Link 0512
(FCM: Fuzzy C-Means) BibRef

Hafiane, A.[Adel], Zavidovique, B.[Bertrand], Chaudhuri, S.,
A Modified FCM with Optimal Peano Scans for Image Segmentation,
ICIP05(III: 840-843).
IEEE DOI Link 0512
BibRef

Hafiane, A.[Adel], Zavidovique, B.[Bertrand],
FCM with Spatial and Multiresolution Constraints for Image Segmentation,
ICIAR05(17-23).
Springer DOI Link 0509
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Seetharaman, G., Bouchafa, S., Zavidovique, B.,
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Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Active Contours, Snakes or Deformable Curves .