Probabilistic saliency approach for elongated structure detection using deformable models

Xavier Orriols, Ricardo Toledo, Xavier Binefa, Petia Radeva, Jordi Vitrià, J. J. Villanueva

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    2 Citations (Scopus)

    Abstract

    In this paper we address the object recognition problem in a probabilistic framework to detect and describe object appearance through image features organized by means of active contour models. We consider the formulation of saliency in terms of visual similarity embedded in the probabilistic principal component analysis framework. A likelihood of object structure detection is obtained using the relation between the visual field and the internal object representation. Deformable models are employed introducing a computational methodology for a perceptual organisation of image features as an abstract understanding of the integration between structure and constraints of the visual information-processing problem. Concrete application of the integrated approach for vessels segmentation in angiography is considered and the results are encouraging. © 2000 IEEE.
    Original languageEnglish
    Pages (from-to)1006-1009
    JournalProceedings - International Conference on Pattern Recognition
    Volume15
    Issue number3
    Publication statusPublished - 1 Dec 2000

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  • Cite this

    Orriols, X., Toledo, R., Binefa, X., Radeva, P., Vitrià, J., & Villanueva, J. J. (2000). Probabilistic saliency approach for elongated structure detection using deformable models. Proceedings - International Conference on Pattern Recognition, 15(3), 1006-1009.