8.7 Combining Region and Edge Based Techniques

Chapter Contents (Back)
Edges, Region Segmentation. Segmentation, Edges. Segmentation, Combined with Edges.

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.[David L.], Herman, M.[Martin],
Clustering Edge Values for Threshold Selection,
CGIP(10), No. 3, July 1979, pp. 272-280.
Elsevier DOI Segmentation, 2-D Histogram. Segmentation, Histogram. Compute histograms of edge information in addition to the intensity (2-D histograms). BibRef 7907

Milgram, D.L.[David L.],
Constructing Trees for Region Description,
CGIP(11), No. 1, September 1979, pp. 88-99.
Elsevier DOI BibRef 7909

Milgram, D.L.[David L.],
Region Extraction Using Convergent Evidence,
CGIP(11), No. 1, September 1979, pp. 1-12.
Elsevier DOI 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.[Satoru],
A Boundary Curve Criterion,
CGIP(11), No. 3, November 1979, pp. 281-289.
Elsevier DOI BibRef 7911

Lemkin, P.[Peter],
The Boundary Trace Transform: An Edge and Region Enhancement Transform,
CGIP(9), No. 2, February 1979, pp. 150-165.
Elsevier DOI 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.
Elsevier DOI 0309
BibRef

Zamperoni, P.,
Contour Tracing of Grey-Scale Images Based on 2-D Histograms,
PR(15), No. 3, 1982, pp. 161-165.
Elsevier DOI 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.[Hemraj],
Reconstruction of Planar Boundaries from Incomplete Information,
CVGIP(39), No. 3, September 1987, pp. 383-387.
Elsevier DOI 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 DOI 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 DOI 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 BibRef 9200

Haddon, J.F.[John F.],
Generalised threshold selection for edge detection,
PR(21), No. 3, 1988, pp. 195-203.
Elsevier DOI 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.
Elsevier DOI Find varying thresholds by keying on edge locations. BibRef 8812

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

Hertz, L.[Lois], and Schafer, R.W.[Ronald 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.
Elsevier DOI 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.
Elsevier DOI 0401
BibRef
Earlier: Roell, S.A.[Stefan A.], Studholme, C.[Colin],
Hierarchical Segmentation Satisfying Constraints,
BMVC94(135-144).
PDF File.
See also Feature-Based Image Analysis. BibRef

Griffin, L.D., Robinson, G.P., Colchester, A.C.F.,
Multiscale Hierarchical segmentation,
BMVC93(xx).
PDF File. 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 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.
Elsevier DOI 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 DOI BibRef 9312
Earlier:
The Integration of Region and Edge-Based Segmentation,
ICCV90(117-120).
IEEE DOI 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.[Satish], 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.
Elsevier DOI 9606
BibRef
Earlier:
Robust representation and recognition of free-form objects,
ICIP96(III: 587-590).
IEEE DOI 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.[Paolo], Lodola, R.[Roberto], Mecocci, A.[Alessandro],
Scene Interpretation by Fusion of Segment and Region Information,
IVC(15), No. 7, July 1997, pp. 499-509.
Elsevier DOI 9708
BibRef

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

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 0008
BibRef
Earlier:
Edge Flow: A Framework of Boundary Detection and Image Segmentation,
CVPR97(744-749).
IEEE DOI
PDF File. 9704
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 0006
BibRef
Earlier:
A fast, accurate method to segment and retrieve object contours in real images,
ICIP96(I: 841-843).
IEEE DOI 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 0011
BibRef
Earlier:
Self-affine Mapping System for Object Contour Extraction,
ICIP99(III:250-254).
IEEE DOI 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.
Elsevier DOI 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.
Elsevier DOI 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.
Elsevier DOI 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.
Elsevier DOI 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.
Elsevier DOI 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.
Elsevier DOI 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.
Elsevier DOI 0211
BibRef
Earlier: A1, A4, A3, A2:
Unsupervised active regions for multiresolution image segmentation,
ICPR02(II: 905-908).
IEEE DOI 0211
BibRef

Bosch, A., Muñoz, X.[Xavier], Freixenet, J.[Jordi],
Segmentation and description of natural outdoor scenes,
IVC(25), No. 5, 1 May 2007, pp. 727-740.
Elsevier DOI 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).
Springer DOI 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 DOI 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 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 DOI 0008
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 Link. 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 0512
BibRef
Earlier:
Bi-directional gradient labeling and registration for gray-scale image segmentation,
ICIP03(I: 365-368).
IEEE DOI 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.
Elsevier DOI 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 0606
BibRef
Earlier:
Parsing Images into Region and Curve Processes,
ECCV02(III: 393 ff.).
Springer DOI 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 0606
Capture both homogenity and nearness to value. BibRef

Chittajallu, D.R., Shah, S.K., Kakadiaris, I.A.,
A shape-driven MRF model for the segmentation of organs in medical images,
CVPR10(3233-3240).
IEEE DOI 1006
BibRef

Brunner, G.[Gerd], Chittajallu, D.R.[Deepak R.], Kurkure, U.[Uday], Kakadiaris, I.A.[Ioannis A.],
Patch-cuts: A Graph-based Image Segmentation Method Using Patch Features and Spatial Relations,
BMVC10(xx-yy).
HTML Version. 1009
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 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.
Elsevier DOI 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).
Springer DOI 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).
Springer DOI 0409
BibRef
And:
Object boundary edge selection using normal direction derivatives of a contour in a complex scene,
ICPR04(IV: 755-758).
IEEE DOI 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.
DOI Link 0702
BibRef

Marot, J., Bourennane, S.,
Propagator method for an application to contour estimation,
PRL(28), No. 12, 1 September 2007, pp. 1556-1562.
Elsevier DOI 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.
Elsevier DOI 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.
Elsevier DOI 0711
Segmentation; Gradient vector flow; Mean shift; Snake BibRef

Zhou, H.Y.[Hui-Yu], Li, X.L.[Xue-Long], Schaefer, G.[Gerald], Celebi, M.E.[M. Emre], Miller, P.[Paul],
Mean shift based gradient vector flow for image segmentation,
CVIU(117), No. 9, 2013, pp. 1004-1016.
Elsevier DOI 1307
Image segmentation 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 0802
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.
DOI Link 0905
BibRef

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.
DOI Link 0909
BibRef

Wang, W.[Wei], Chung, C.K.R.[Chi-Kit Ronald],
Image Segmentation With Complementary Use Of Edge And Region Information,
IJIG(11), No. 4, October 2011, pp. 549-570.
DOI Link 1201
BibRef
Earlier:
Image Segmentation That Optimizes Global Homogeneity in a Variational Framework,
ISVC07(II: 52-61).
Springer DOI 0711
BibRef
Earlier:
Image Segmentation That Merges Together Boundary and Region Information,
ACCV06(I:226-235).
Springer DOI 0601
BibRef
Earlier:
The Multiplicative Path Toward Prior-Shape Guided Active Contour for Object Detection,
ISVC07(II: 539-548).
Springer DOI 0711
BibRef
Earlier:
Image segmentation via brittle fracture mechanics,
ICIP04(II: 909-912).
IEEE DOI 0505
BibRef

Mora, M.[Marco], Córdova-Lepe, F.[Fernando], Del-Valle, R.[Rodrigo],
A non-Newtonian gradient for contour detection in images with multiplicative noise,
PRL(33), No. 10, 15 July 2012, pp. 1245-1256.
Elsevier DOI 1205
Non-Newtonian gradient; Multiplicative gradient; Contour detection; Multiplicative noise BibRef

Chen, F.[Fei], Yu, H.M.[Hui-Min], Hu, R.[Roland],
Shape Sparse Representation for Joint Object Classification and Segmentation,
IP(22), No. 3, March 2013, pp. 992-1004.
IEEE DOI 1302
BibRef

Yao, J.C.[Jin-Cao], Yu, H.M.[Hui-Min], Hu, R.[Roland],
Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework,
JOSA-A(34), No. 1, January 2017, pp. 27-38.
DOI Link 1701
Digital image processing; Image analysis BibRef

Yao, J.C.[Jin-Cao], Yu, H.M.[Hui-Min], Hu, R.[Roland],
A new sparse representation-based object segmentation framework,
VC(33), No. 2, February 2017, pp. 179-192.
WWW Link. 1702
BibRef

Chen, F.[Fei], Yu, H.M.[Hui-Min], Hu, R.[Roland], Zeng, X.[Xunxun],
Deep Learning Shape Priors for Object Segmentation,
CVPR13(1870-1877)
IEEE DOI 1309
Boltzmann machine BibRef

Zhang, W.J.[Wen Juan], Feng, X.C.[Xiang Chu], Han, Y.[Yu],
A novel image segmentation model with an edge weighting function,
SIViP(8), No. 1, January 2014, pp. 121-132.
WWW Link. 1402
BibRef

Han, Y.[Yu], Wang, W.W.[Wei-Wei], Feng, X.C.[Xiang-Chu],
A new fast multiphase image segmentation algorithm based on nonconvex regularizer,
PR(45), No. 1, 2012, pp. 363-372.
Elsevier DOI 1410
Image segmentation BibRef

Paxman, R.G.[Richard G.], Carrara, D.A.[David A.], Walker, P.D.[Paul D.], Davidenko, N.[Nicolas],
Silhouette estimation,
JOSA-A(31), No. 7, July 2014, pp. 1636-1644.
DOI Link 1407
Deconvolution. Restoration beyound the diffraction limit. BibRef

Boukerroui, D.[Djamal],
Efficient numerical schemes for gradient vector flow,
PR(45), No. 1, 2012, pp. 626-636.
Elsevier DOI 1410
BibRef
Earlier: ICIP09(4057-4060).
IEEE DOI 0911
Active contours BibRef

Liu, J.[Jing], Li, P.J.[Pei-Jun], Wang, X.[Xue],
A new segmentation method for very high resolution imagery using spectral and morphological information,
PandRS(101), No. 1, 2015, pp. 145-162.
Elsevier DOI 1503
Very high resolution image BibRef

Liu, J.[Jing], Li, P.J.[Pei-Jun], Zhang, J.[Jun], Guo, J.C.[Jian-Cong],
Very high resolution Image Segmentation by combined spectral and structural information,
CVRS12(24-29).
IEEE DOI 1302
BibRef

Chen, B.[Bo], Qiu, F.[Fang], Wu, B.F.[Bing-Fang], Du, H.Y.[Hong-Yue],
Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty,
RS(7), No. 5, 2015, pp. 5980-6004.
DOI Link 1506
BibRef

Gong, M.L.[Mei-Ling], Lan, J.H.[Jin-Hui], Yang, C.L.[Chang-Lin], Wu, H.T.[Hong-Tao], Zhi, T.[Tao],
Adaptive image segmentation algorithm under the constraint of edge posterior probability,
IET-CV(11), No. 8, December 2017, pp. 702-709.
DOI Link 1712
BibRef

Zhang, W.H.[Wei-Hang], Wang, X.[Xue], You, W.[Wei], Chen, J.F.[Jun-Feng], Dai, P.[Peng], Zhang, P.B.[Peng-Bo],
RESLS: Region and Edge Synergetic Level Set Framework for Image Segmentation,
IP(29), No. 1, 2020, pp. 57-71.
IEEE DOI 1910
image segmentation, optimisation, set theory, hybrid level set models, region information, edge-based model, hybrid models BibRef

Zhang, W.H.[Wei-Hang], Li, H.Q.[Hui-Qi],
Image Segmentation With Adaptive Edge-Region Collaborated Level-Set Method,
SPLetters(31), 2024, pp. 2105-2109.
IEEE DOI 2408
Image edge detection, Adaptation models, Entropy, Computational modeling, Image segmentation, Iterative methods, level-set models BibRef

Meng, Y.[Yanda], Zhang, H.R.[Hong-Run], Zhao, Y.T.[Yi-Tian], Yang, X.Y.[Xiao-Yun], Qiao, Y.H.[Yi-Hong], MacCormick, I.J.C.[Ian J. C.], Huang, X.W.[Xiao-Wei], Zheng, Y.L.[Ya-Lin],
Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation,
MedImg(41), No. 3, March 2022, pp. 690-701.
IEEE DOI 2203
Image segmentation, Feature extraction, Cognition, Task analysis, Semantics, Optical imaging, Optical computing, Region-boundary, segmentation BibRef

Yu, L.T.[Le-Tian], Mei, H.Y.[Hai-Yang], Dong, W.[Wen], Wei, Z.Q.[Zi-Qi], Zhu, L.[Li], Wang, Y.X.[Yu-Xin], Yang, X.[Xin],
Progressive Glass Segmentation,
IP(31), No. 2022, pp. 2920-2933.
IEEE DOI 2204
Glass, Feature extraction, Image segmentation, Semantics, Task analysis, Bridges, Aggregates, Glass segmentation, deep neural network BibRef

Zhang, Y.H.[Yu-Hang], Tian, S.[Shishun], Liao, M.[Muxin], Hua, G.G.[Guo-Guang], Zou, W.B.[Wen-Bin], Xu, C.[Chen],
Learning Shape-Invariant Representation for Generalizable Semantic Segmentation,
IP(32), 2023, pp. 5031-5045.
IEEE DOI 2310
BibRef

Huang, W.[Wei], Zhao, Y.H.[Yu-Hao], Sun, L.[Le], Gao, L.[Lu], Chen, Y.[Yuwen],
A Novel Adaptive Edge Aggregation and Multiscale Feature Interaction Detector for Object Detection in Remote Sensing Images,
RS(15), No. 21, 2023, pp. 5200.
DOI Link 2311
BibRef

He, H.[Hao], Li, X.T.[Xiang-Tai], Cheng, G.L.[Guang-Liang], Shi, J.P.[Jian-Ping], Tong, Y.H.[Yun-Hai], Meng, G.F.[Gao-Feng], Prinet, V.[Véronique], Weng, L.B.[Lu-Bin],
Enhanced Boundary Learning for Glass-like Object Segmentation,
ICCV21(15839-15848)
IEEE DOI 2203
Shape, Convolution, Navigation, Semantics, Object segmentation, Predictive models, Robot sensing systems, grouping and shape BibRef

Liu, A.[Anran], Huang, X.S.[Xiang-Sheng], Li, T.[Tong], Ma, P.C.[Peng-Cheng],
Co-Net: A Collaborative Region-Contour-Driven Network for Fine-to-Finer Medical Image Segmentation,
WACV22(1706-1715)
IEEE DOI 2202
Image segmentation, Satellite broadcasting, Redundancy, Collaboration, Process control, Feature extraction, Robustness, Vision for Graphics BibRef