Edgeflow-driven Variational Image Segmentation: Theory and Performance Evaluation

Baris Sumengen, B. S. Manjunath
Department of Electrical and Computer Engineering
University of California, Santa Barbara, CA
{sumengen, manj} [at] ece.ucsb.edu


We introduce robust variational segmentation techniques that are driven by an Edgeflow vector field. Variational image segmentation has been widely used during the past ten years. While there is a rich theory of these techniques in the literature, a detailed performance analysis on real natural images is needed to compare the various methods proposed. In this context, this paper makes the following contributions: (a) designing curve evolution and anisotropic diffusion methods that use Edgeflow vector fields to obtain good quality segmentation results over a large and diverse class of images, and (b) a detailed experimental evaluation of these segmentation methods. Our experiments show that Edgeflowbased anisotropic diffusion outperforms other competing methods by a significant margin.
[PDF] [BibTex]
B. Sumengen and B. S. Manjunath,
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), May. 2005.
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Subject: [Detection on Images and Videos] « Look up more