Abstract
Compact feature-based image coding as well as view-based object representations require a preprocessing step that abstracts from image details while preserving essential signal structures. Variational segmentation and nonlinear diffusion approaches provide powerful methods for the design of such a preprocessing stage. This motivates two investigate parallel numerical schemes to enable preprocessing of large image databases in a reasonable amount of time.
In the present paper we consider a non-quadratic convex variational approach for image segmentation and feature extraction. A class of iterative numerical algorithms is defined that allow for the efficient computation of the unique minimum. These algorithms converge globally and do not depend on the starting point. This is an important feature for (semi-)automated image processing and unsupervised feature extraction tasks. We show that our class covers also two-step optimization approaches that have been proposed in the recent literature in the context of image segmentation and restoration. Empirical results of the performance of various iterative numerical schemes on a parallel architecture are also presented.
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Heers, J., Schnörr, C., Stiehl, H.S. (1998). A Class of Parallel Algorithms for Nonlinear Variational Segmentation: A preprocess for robust feature-based image coding. In: Marshall, S., Harvey, N.R., Shah, D. (eds) Noblesse Workshop on Non-Linear Model Based Image Analysis. Springer, London. https://doi.org/10.1007/978-1-4471-1597-7_23
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DOI: https://doi.org/10.1007/978-1-4471-1597-7_23
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