Connected components in binary images: the detection problem
Connected components in binary images: the detection problem
Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer processing of line images: a survey
Pattern Recognition
A contour processing method for fast binary neighbourhood operations
Pattern Recognition Letters
Analysis of Thinning Algorithms Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Glossary of computer vision terms
Pattern Recognition
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Connectivity in Digital Pictures
Journal of the ACM (JACM)
Some Parallel Thinning Algorithms for Digital Pictures
Journal of the ACM (JACM)
Digital Picture Processing
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
A List-Processing Approach to Compute Voronoi Diagrams and the Euclidean Distance Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Reversible Contour Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Original paper: Real time feature extraction and Standard Cutting Models fitting in grape leaves
Computers and Electronics in Agriculture
Removing interference components in time-frequency representations using morphological operators
Journal of Visual Communication and Image Representation
Hi-index | 0.14 |
Two algorithms for skeletonization of 2-D binary images, each of which explicitly separates the two major aspects of skeletonization are described: the identification of points critical to shape representation, and the identification of further points necessary to preserve homotopy. Sets of points critical to shape representation are found by eroding the original image I with a nested sequence of structuring elements E/sub i/. By choosing appropriate (E/sub i/) and D, a structuring element, either algorithm is capable of producing a variety of skeletons corresponding to different distance functions. A sufficient condition is given for the original image to be reconstructed from the skeleton. In the case of the first algorithm, there are few restrictions on the set of structuring elements. It uses a simple search strategy to find points whose removal would alter homotopy. The second, faster, algorithm has a more constructional approach to finding points necessary for preserving homotopy, which limits it to a more restricted set of structuring elements than the first algorithm. However, it may still be used with a variety of distance functions.