Principal curve detection in complicated graph images

Yuncai Liu, Thomas S. Huang

Research output: Contribution to journalConference articlepeer-review

Abstract

Finding principal curves in an image is an important low level processing in computer vision and pattern recognition. Principal curves are those curves in an image that represent boundaries or contours of objects of interest. In general, a principal curve should be smooth with certain length constraint and allow either smooth or sharp turning. In this paper, we present a method that can efficiently detect principal curves in complicated map images. For a given feature image, obtained from edge detection of an intensity image or thinning operation of a pictorial map image, the feature image is first converted to a graph representation. In graph image domain, the operation of principal curve detection is performed to identify useful image features. The shortest path and directional deviation schemes are used in our algorithm of principal verve detection, which is proven to be very efficient working with real graph images.

Original languageEnglish (US)
Pages (from-to)201-212
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4552
StatePublished - Dec 1 2001
Externally publishedYes
EventImage Matching and Analysis - Wuhan, China
Duration: Oct 22 2001Oct 24 2001

Keywords

  • Document Processing
  • Feature abstraction
  • Graph
  • Image segmentation
  • Principal curve

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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