IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of two-dimensional boundaries using the chain code
Pattern Recognition
Review of shape coding techniques
Image and Vision Computing
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimum polygonal approximation of digitized curves
Pattern Recognition Letters
Parts of Visual Form: Computational Aspects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Another look at the dominant point detection of digital curves
Pattern Recognition Letters
Convexity rule for shape decomposition based on discrete contour evolution
Computer Vision and Image Understanding
A boundary concavity code to support dominant point detection
Pattern Recognition Letters
Computer Processing of Line-Drawing Images
ACM Computing Surveys (CSUR)
An efficient algorithm for the optimal polygonal approximation of digitized curves
Pattern Recognition Letters
Locating Perceptually Salient Points on Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indentation and Protrusion Detection and Its Applications
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Shape partitioning by convexity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
To Boldly Split: Partitioning Space Filling Curves by Markov Chain Monte Carlo Simulation
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Morphological multiscale decomposition of connected regions with emphasis on cell clusters
Computer Vision and Image Understanding
On the decomposition of cell clusters
Journal of Mathematical Imaging and Vision
Journal of Visual Communication and Image Representation
Shape matching using coarse descriptors
International Journal of Computational Vision and Robotics
Shape Codification Indexing and Retrieval Using the Quad-Tree Structure
International Journal of Computer Vision and Image Processing
| Hi-index | 0.10 |
A new method based on parsing the concavity code is used to partition a digital contour into concave and convex sections. Innovative features of the technique include: (1) coerced two-state classification of every point into a concavity or a convexity; (2) linguistic explanation for each point's classification; (3) curvature validation via the residue (that portion of the boundary remaining after curvature extraction); (4) preservation of original contour shape; (5) symbolic logic methodology (no floating point operations); (6) parameter-free implementation; (7) linear time and space complexity. No other currently available contour partitioning method exhibits all of these features. The parser achieves two-state partitioning by implementing simple notions of cumulative curvature, vertex adjacency, shallow curvature absorption, and residue sharing. Concavities and convexities are color-coded to help disambiguate complex images such as topographic contour maps and radio frequency propagation plots. To gauge product quality, the observer may appeal visually to the residue for validation.