Active contours without edges for vector-valued images

Tony Chan*, B. Yezrielev Sandberg, Luminita A. Vese

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

582 Scopus citations

Abstract

In this paper, we propose an active contour algorithm for object detection in vector-valued images (such as RGB or multispectral). The model is an extension of the scalar Chan-Vese algorithm to the vector-valued case [1]. The model minimizes a Mumford-Shah functional over the length of the contour, plus the sum of the fitting error over each component of the vector-valued image. Like the Chan-Vese model, our vector-valued model can detect edges both with or without gradient. We show examples where our model detects vector-valued objects which are undetectable in any scalar representation. For instance, objects with different missing parts in different channels are completely detected (such as occlusion). Also, in color images, objects which are invisible in each channel or in intensity can be detected by our algorithm. Finally, the model is robust with respect to noise, requiring no a priori denoising Step.

Original languageEnglish (US)
Pages (from-to)130-141
Number of pages12
JournalJournal of Visual Communication and Image Representation
Volume11
Issue number2
DOIs
StatePublished - Jan 1 2000
Event2nd International Conference on Scale Space Theory in Computer Vision - Corfu, Greece
Duration: Sep 26 1999Sep 27 1999

Keywords

  • Active contours
  • Level sets
  • Object detection
  • PDEs
  • Segmentation
  • Vector-valued images

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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