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A new image segmentation algorithm with applications to image inpainting

Author

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  • Ojeda, Silvia
  • Vallejos, Ronny
  • Bustos, Oscar

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.

Suggested Citation

  • Ojeda, Silvia & Vallejos, Ronny & Bustos, Oscar, 2010. "A new image segmentation algorithm with applications to image inpainting," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2082-2093, September.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:9:p:2082-2093
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    References listed on IDEAS

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    1. Baran, Sándor & Pap, Gyula & van Zuijlen, Martien C. A., 2004. "Asymptotic inference for a nearly unstable sequence of stationary spatial AR models," Statistics & Probability Letters, Elsevier, vol. 69(1), pages 53-61, August.
    2. Jiin‐Huarng Guo & L. Billard, 1998. "Some Inference Results for Causal Autoregressive Processes on a Plane," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(6), pages 681-691, November.
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    Cited by:

    1. Palm, Bruna G. & Bayer, Fábio M. & Cintra, Renato J., 2022. "2-D Rayleigh autoregressive moving average model for SAR image modeling," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    2. Grisel Maribel Britos & Silvia María Ojeda, 2019. "Robust estimation for spatial autoregressive processes based on bounded innovation propagation representations," Computational Statistics, Springer, vol. 34(3), pages 1315-1335, September.
    3. Rulloni, Valeria & Bustos, Oscar & Flesia, Ana Georgina, 2012. "Large gap imputation in remote sensed imagery of the environment," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2388-2403.

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