We introduce a method for edge detection based on clustering of the pixels representing any given digital image into two sets (the edge pixels and the non-edge ones). The process associates with each pixel a vector representing the dierences in luminosity w.r.t. the surrounding pixels. Clustering is driven by the norms of such vectors and we adopt a parsimonious optimization algorithm to detect the required two clusters. We use DC (Dierence of Convex) decomposition of a classic nonsmooth nonconvex model function for clustering. The results for some benchmark images are reported.
Edge detection via clustering
2015
Abstract
We introduce a method for edge detection based on clustering of the pixels representing any given digital image into two sets (the edge pixels and the non-edge ones). The process associates with each pixel a vector representing the dierences in luminosity w.r.t. the surrounding pixels. Clustering is driven by the norms of such vectors and we adopt a parsimonious optimization algorithm to detect the required two clusters. We use DC (Dierence of Convex) decomposition of a classic nonsmooth nonconvex model function for clustering. The results for some benchmark images are reported.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
