Journal
PATTERN RECOGNITION
Volume 48, Issue 8, Pages 2513-2529Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2015.03.001
Keywords
Active contour; Level set; Natural image segmentation; Intensity inhomogeneity; Saliency-inspired initial contour
Funding
- National Science Fund for Distinguished Young Scholars [61125305, 91420201, 61472187, 61233011, 61373063]
- National Natural Science Fund of China [61103058]
- Key Project of Chinese Ministry of Education [313030]
- 973 Program [2014CB349303]
- Fundamental Research Funds for the Central Universities [30920140121005]
- Program for Chang-jiang Scholars and Innovative Research Team in University [IRT13072]
Ask authors/readers for more resources
Active contour model (ACM) is one of the popular methodologies for image segmentation. However, the ACMs developed so far have not shown powerful performance on natural images. The reason is that natural images are rich in color, intensity or texture. The object pixels are often not artifact inhomogeneous, but inherently inhomogeneous. In this paper, we propose an inhomogeneity-embedded active contour (InH_ACM) for natural image segmentation. InH_ACM describes the inhomogeneity in natural images by a pixel inhomogeneity factor and utilizes it for segmentation, unlike most of existing methods that use some averaging convolution to reduce or remove the inhomogeneity in images. Moreover, we build a saliency-inspired framework that can automatically locate the initial contour for InH_ACM to start the evolution. Experimental results on Alpert's 100 gray images, MSRA's 1000 color images and our collected 300 images where the contained objects are mostly intrinsic inhomogeneous indicate that our proposed InH_ACM can produce reliably satisfactory segmentation in many situations, outperforming most of current popular ACMs. (C) 2015 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available