Review on shape matching

Zidonghua Xuebao/Acta Automatica Sinica

Volumn 27, Issue 5, 2001, Pages 678-694

  Ding, Xian Feng ^a   Wu, Hong ^a   Zhang, Hong Jiang ^a   Ma, Song De ^a  

^a INSTITUTE OF AUTOMATION   (China)

Author keywords

Deformable model; Pattern recognition; Shape matching; Shape simplification

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER VISION; CONTENT BASED RETRIEVAL; DEFORMATION; DYNAMIC PROGRAMMING; FEATURE EXTRACTION; INVARIANCE; MATHEMATICAL MODELS; MATHEMATICAL TECHNIQUES; PATTERN MATCHING;

DEFORMABLE MODELS; LOCAL FEATURE; MARKOV MODELS; SHAPE MATCHING; SHAPE REPRESENTATION; SHAPE SIMPLIFICATION;

PATTERN RECOGNITION;

EID: 0035467188     PISSN: 02544156     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (132)

1. Axial representations of shape
2. Hierarchic voronoi skeletons
3. Extraction of shape skeletons from grayscale images
4. Mathematic theories of shape: Do they model perception?
5. On the sensitivity of Hough transform for object recognition
6. Shape recognition using the straight line Hough transform: Theory and generalization
7. Partial shape recognition using dynamic programming
8. ψ-s correlation and dynamic time warping: Two methods for tracking ice floes in sar image
9. On curve matching
10. On the distribution of a deforming triangle
11. Affine invariant model-based object recognition
12. Object matching using deformable templates
13. Deformable prototypes for encoding shape categories in image database
14. Visual image retrieval by elastic matching of user sketches
15. Boundary finding with correspondence using statistical shape models
16. Deformable contours: Modeling and extraction
17. Genetic algorithm for partial shape recognition
18. Shape matching using Schwarz representation
19. On the encoding of arbitrary geometric configurations
20. Comparative analysis of line drawing modeling schemes
21. Shape description via the use of critical points
22. Computer processing of line drawing images
23. Second generation image coding techniques
24. Algorithms for image/vector conversion
25. Locating perceptually salient points on planar curves
26. Computer recognition of partial views of curved objects
27. Shape design, representation, and restoration with splines
28. Invariant matching and identification of curves using B-splines curve representation
29. Periodic Quasi-orthogonal spline bases and applications to least-squares curve fitting of digital images
30. B-spline contour representation and symmetry detection
31. Discrete binomial splines
32. Efficient algorithms for the implementation of general B-splines
33. Fast B-spline transforms for continuous image representation and interpolation
34. Describing complicated objects by implicit polynomials
35. Describing complicated objects by implicit polynomials
36. Generalized multidimensional orthogonal polynomials with applications to shape analysis
37. Curve fitting by a sum of Gaussians
38. Polygonal approximations by Newton's method
39. Shape representation by multiscale contour approximation
40. Scale space filtering
41. Polygonal approximation using competitive Hopfield neural network
42. Schwarz scale space theory and its application
43. Uniqueness of Gaussian kernel for scale space filtering
44. The curvature primal sketch
45. Scale-based description and recognition of planar curves and two dimensional shapes
46. A multiscaling approach based on morphological filtering
47. Decomposition of arbitrarily shaped binary morphological structuring elements using genetic algorithm
48. A morphological operator for corner detection
49. Affine plane curve evolution: A fully consistent scheme
50. Comparison between the morphological skeleton and morphological shape decomposition
51. Image retrieval based on multidimensional feature properties
52. Pattern recognition by moment invariant
53. Image analysis via the general theory of moments
54. Orthogonal moment feature for use with parametric and non-parametric classifiers
55. Moment invariants for pattern recognition
56. Fourier descriptors for plane closed curves
57. Shape discrimination using Fourier descriptors
58. Wavelet descriptor of planar curves: Theory and application
59. Automatic target shape recognition via deformable wavelet templates
60. Recognition of 2D object contours using the wavelet transform zero-crossing representation
61. Invariant Fourier wavelet descriptor for pattern recognition
62. Recognition of 2D object contours using starting point independent wavelet coefficient matching
63. Morphological shape description using geometric spectrum on multidimensional binary images
64. Two-dimensional shape representation using morphological correlation function
65. A morphological signature transform for shape description
66. Near-Optimal MST-based shape description using genetic algorithm
67. Generalizing the Hough transform to detect arbitrary shapes
68. Object recognition using multilayer Hopfield neural network
69. Recognition of partially occluded objects using neural network based indexing
70. Curvature scale space driven object recognition with an indexing scheme based on artificial neural networks
71. The rubber mask technique, Parts I and II
72. The representation and matching of pictorial structure
73. Snakes: Active contour models
74. Making snakes converge from minimal initialization
75. Imposing hard constraints on soft snakes
76. Note on active contour models and balloons
77. Using dynamic programming for solving variational problems in vision
78. Dynamic programming for detecting, tracking, and matching deformable contours
79. Deformable template using large deformation kinematics
80. Region-based strategies for active contour models
81. Adaptive B-splines and boundary estimation
82. 'Ratio regions' a technique for image segmentation
83. B-Snakes: Implementation and application to stereo
84. Intelligent interactive image outlining using spline snake
85. Baysian estimation of ventricular contour in angiographic images
86. Feature extraction from faces using deformable templates
87. Detecting straight edge in millimeter wave images
88. Boundary finding with parametrically deformable models
89. Towards automated image understanding,
90. Structural image restoration through deformable template
91. Use of active shape models for locating structures in medical images
92. Active shape models-their training and application
93. Segmentation and interpretation of MR brain images using an improved knowledge based active shape model
94. A hieraachical statistical framework for the segmentation of deformable objects in image sequences
95. Modal matching for correspondence and recognition
96. Measuring concavity on a rectangular mosaic
97. On the cellular convexity of complexes
98. Digital convexity, straightness and convex polygons
99. Digital and cellular convexity
100. Definition of convexity and convex hulls in digital images
101. The ultimate planar convex hull algorithm?
102. On the finding the convex hull of a simple polygon
103. A lower bound to finding the convex hull
104. Hirearchical shape description based upon convex hulls of concavities
105. Learning structural descriptions from examples
106. Pattern recognition as rule guided inductive inference
107. Matching learning: Technique and foundations
108. Learning shape descriptions
109. Model learning and recognition of nongrid objects
110. A comparative review of selected methods for learning from examples
111. Learning visual models from shape contours using multiscale convex/concave structure matching
112. A bibliography on digital and computational convexity (1961-1988)
113. Representation of feature point and error analysis
114. The string to string correction problem
115. Two dimensional object recognition through two stage string matching
116. Stochastic models for closed boundary analysis: Representation and reconstruction
117. An autoregressive model approach to two dimensional shape classification
118. A shape description by time series
119. A bivariate autoregressive modeling technique for analysis
120. Complex autoregressive model for shape recognition
121. 2-D shape classification using hidden Markov model