Journal Article10.1109/34.56190
Analysis of thinning algorithms using mathematical morphology
B.-K. Jang,Roland T. Chin +1 more
207
TL;DR: A precise definition of digital skeletons and a mathematical framework for the analysis of a class of thinning algorithms, based on morphological set transformation, are presented and an algorithm based on this condition is developed.
read more
Abstract: A precise definition of digital skeletons and a mathematical framework for the analysis of a class of thinning algorithms, based on morphological set transformation, are presented. A particular thinning algorithm (algorithm A) is used as an example in the analysis. Precise definitions and analyses associated with the thinning process are presented, including the proof of convergence, the condition for one-pixel-thick skeletons, and the connectedness of skeletons. In addition, a necessary and sufficient condition for the thinning process in general is derived, and an algorithm (algorithm B) based on this condition is developed. Experimental results are used to compare the two thinning algorithms, and issues involving noise immunity and skeletal bias are addressed. >
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Book
Algorithms for image processing and computer vision
James R. Parker
- 25 Nov 1996
TL;DR: Algorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications.
A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas
A.K. Shackelford,Curt H. Davis +1 more
TL;DR: An object-based approach for urban land cover classification from high-resolution multispectral image data that builds upon a pixel-based fuzzy classification approach is presented and is able to identify buildings, impervious surface, and roads in dense urban areas with 76, 81, and 99% classification accuracies.
Analysis of infected blood cell images using morphological operators
TL;DR: A morphological approach to cell image segmentation, that is, more accurate than the classical watershed-based algorithm, is introduced for detecting and classifying malaria parasites in images of Giemsa stained blood slides.
309
Automated Road Information Extraction From Mobile Laser Scanning Data
TL;DR: This paper describes the development of automated algorithms for extracting road features (road surfaces, road markings, and pavement cracks) from MLS point cloud data and concludes that MLS is a reliable and cost-effective alternative for rapid road inspection.
174
References
•Book
Image Analysis and Mathematical Morphology
Jean Serra
- 11 Feb 1984
TL;DR: This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
10.1K
•Book
Digital Picture Processing
Azriel Rosenfeld,Avinash C. Kak +1 more
- 01 Jan 1976
TL;DR: The rapid rate at which the field of digital picture processing has grown in the past five years had necessitated extensive revisions and the introduction of topics not found in the original edition.
5.1K
Image Analysis Using Mathematical Morphology
TL;DR: The tutorial provided in this paper reviews both binary morphology and gray scale morphology, covering the operations of dilation, erosion, opening, and closing and their relations.
2.9K