Pairwise similarity for line extraction from distorted images

Hideitsu Hino, Jun Fujiki, Shotaro Akaho, Yoshihiko Mochizuki, Noboru Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Clustering a given set of data is crucial in many fields including image processing. It plays important roles in image segmentation and object detection for example. This paper proposes a framework of building a similarity matrix for a given dataset, which is then used for clustering the dataset. The similarity between two points are defined based on how other points distribute around the line connecting the two points. It can capture the degree of how the two points are placed on the same line. The similarity matrix is considered as a kernel matrix of the given dataset, and based on it, the spectral clustering is performed. Clustering with the proposed similarity matrix is shown to perform well through experiments using an artificially designed problem and a real-world problem of detecting lines from a distorted image.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages250-257
Number of pages8
Volume8048 LNCS
EditionPART 2
DOIs
Publication statusPublished - 2013
Event15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013 - York
Duration: 2013 Aug 272013 Aug 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8048 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013
CityYork
Period13/8/2713/8/29

Fingerprint

Image segmentation
Pairwise
Image processing
Line
Clustering
Experiments
Spectral Clustering
Object Detection
Image Segmentation
Image Processing
kernel
Similarity
Object detection
Experiment

Keywords

  • distorted image
  • line detection
  • pairwise similarity
  • spectral clustering

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Hino, H., Fujiki, J., Akaho, S., Mochizuki, Y., & Murata, N. (2013). Pairwise similarity for line extraction from distorted images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 8048 LNCS, pp. 250-257). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8048 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-40246-3_31

Pairwise similarity for line extraction from distorted images. / Hino, Hideitsu; Fujiki, Jun; Akaho, Shotaro; Mochizuki, Yoshihiko; Murata, Noboru.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8048 LNCS PART 2. ed. 2013. p. 250-257 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8048 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hino, H, Fujiki, J, Akaho, S, Mochizuki, Y & Murata, N 2013, Pairwise similarity for line extraction from distorted images. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 8048 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8048 LNCS, pp. 250-257, 15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013, York, 13/8/27. https://doi.org/10.1007/978-3-642-40246-3_31
Hino H, Fujiki J, Akaho S, Mochizuki Y, Murata N. Pairwise similarity for line extraction from distorted images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 8048 LNCS. 2013. p. 250-257. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-40246-3_31
Hino, Hideitsu ; Fujiki, Jun ; Akaho, Shotaro ; Mochizuki, Yoshihiko ; Murata, Noboru. / Pairwise similarity for line extraction from distorted images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8048 LNCS PART 2. ed. 2013. pp. 250-257 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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