A new image segmentation algorithm with applications to image inpainting

Authors:
Silvia Ojeda;Ronny Vallejos;Oscar Bustos
Affiliations:
Facultad de Matemática, Astronomía y Física, Universidad Nacional de Córdoba, Haya de la Torre y Medina Allende S/N, Código Postal 5000, Córdoba, Argentina;Departamento de Matemática, Universidad Técnica Federico Santa María, Casilla 110-V, Valparaíso, Chile;Facultad de Matemática, Astronomía y Física, Universidad Nacional de Córdoba, Haya de la Torre y Medina Allende S/N, Código Postal 5000, Córdoba, Argentina
Venue:
Computational Statistics & Data Analysis
Year:
2010

Citing 8
Cited 1

Estimation of time series parameters in the presence of outliers

Technometrics
Data clustering: a review

ACM Computing Surveys (CSUR)
Image inpainting

Proceedings of the 27th annual conference on Computer graphics and interactive techniques
A non-parametric filter for digital image restoration, using cluster analysis

Pattern Recognition Letters
Unsupervised texture segmentation/classification using 2-D autoregressive modeling and the stochastic expectation-maximization algorithm

Pattern Recognition Letters
Parameter estimation of two-dimensional moving average randomfields

IEEE Transactions on Signal Processing
Region filling and object removal by exemplar-based image inpainting

IEEE Transactions on Image Processing
Morphology-based multifractal estimation for texture segmentation

IEEE Transactions on Image Processing

Large gap imputation in remote sensed imagery of the environment

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

This article describes a new approach to perform image segmentation. First an image is locally modeled using a spatial autoregressive model for the image intensity. Then the residual autoregressive image is computed. This resulting image possesses interesting texture features. The borders and edges are highlighted, suggesting that our algorithm can be used for border detection. Experimental results with real images are provided to verify how the algorithm works in practice. A robust version of our algorithm is also discussed, to be used when the original image is contaminated with additive outliers. A novel application in the context of image inpainting is also offered.