CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 1995 vol.17 Issue No.06 - June
Issue No.06 - June (1995 vol.17)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.387509
<p><it>Abstract</it>—The techniques of a posteriori image restoration and itera- tive image feature extraction are described and compared. Image feature extraction methods known as Graduated Nonconvexity (GNC), Variable Conductance Diffusion (VCD), Anisotropic Diffusion, and Biased Anisotropic Diffusion (BAD), which extract edges from noisy images, are compared with a restoration/feature extraction method known as Mean Field Annealing (MFA). All are shown to be performing the same basic operation: image relaxation. This equivalence shows the relationship between energy minimization methods and spatial analysis methods and between their respective parameters of temperature and scale. As a result of the equivalence, VCD is demonstrated to minimize a cost function, and that cost is specified explicitly. Furthermore, operations over scale space are shown to be a method of avoiding local minima.</p>
Graduated nonconvexity, anisotropic diffusion, mean field annealing, image optimization.
Youn-Sik Han, Griff Bilbro, Ross Whitaker, Stephen Pizer, "Image Relaxation: Restoration and Feature Extraction", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.17, no. 6, pp. 620-624, June 1995, doi:10.1109/34.387509