Abstract
Image analysis is directly applicable to various important and sensitive societal sectors, such as medicine, defense, and security. Often, related research is funded under industrial projects with tight deadlines. Therefore, sometimes certain important theoretical issues remain unaddressed. Such issues have been discussed in length in a recent article [2]. The latter suggested a number of strategic objectives for theoretical research in combinatorial image analysis. Most of these relate to the need to make the discipline better integrated within a number of well-established subjects of theoretical computer science and discrete applied mathematics, such as theory of algorithms and problem complexity, combinatorial optimization and polyhedral combinatorics, integer and linear programming, and computational geometry. Here we concern more in detail one aspect of the research on combinatorial algorithms for image analysis.
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Brimkov, V.E. (2011). Complexity and Approximability Issues in Combinatorial Image Analysis. In: Aggarwal, J.K., Barneva, R.P., Brimkov, V.E., Koroutchev, K.N., Korutcheva, E.R. (eds) Combinatorial Image Analysis. IWCIA 2011. Lecture Notes in Computer Science, vol 6636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21073-0_2
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DOI: https://doi.org/10.1007/978-3-642-21073-0_2
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