A new image segmentation algorithm with applications to image inpainting

Image Segmentation and Time Series Clustering Based on Spatial and Temporal ARMA Processes

“…Thus, to implement an algorithm based on these notions, we must characterize which patterns are present in the residual image when the fitted image is not a good representation of the original one, and we must develop a technique to produce a fitting that is satisfactory in terms of segmentation but not a very good estimation in that the residual image still contains valuable information. (Ojeda et al 2010) investigated these concerns and, based on several numerical experiments with images, determined that the residual image associated with a good local fitting is in fact poor in terms of structure (i.e., it is very similar to a white noise). However, when the fitted image is poor in terms of estimation, the residual image is useful for highlighting the boundaries and edges of the original image.…”

“…In all experiments carried out in (Ojeda et al, 2010) and (Quintana et al, 2011), Algorithm 1 was implemented using the same prediction window for the AR-2D process, which contains only two elements belonging to a strongly causal region on the plane. Here, we consider other prediction windows to observe the effect on the performance of Algorithm 2.…”

“…Several theoretical contributions have been suggested in the literature, including the asymptotic properties of a nearly unstable sequence of stationary spatial autoregressive processes (Baran et al, 2004). Other contributions and applications of spatial ARMA processes have been considered in many publications (Basu & Reinsel, 1993 A new approach to perform image segmentation based on the estimation of AR-2D processes has been recently suggested (Ojeda 2010). First an image is locally modeled using a spatial autoregressive model for the image intensity.…”

“…In the first part we review some theoretical aspects of the spatial ARMA processes. Then the algorithm suggested by Ojeda(2010), its limitations and advantages are briefly described. In order to propose a more efficient algorithm new variants of this algorithm are suggested specially to address the problem of determining the most convenient (in terms of the quality of the segmentation) prediction window of unilateral AR-2D processes.…”

“…The best performance is attained for the maximum block size, which would be the size of the original image. The image segmentation algorithm introduced by (Ojeda et al 2010) can be summarized as follows.…”