CPM 2013

24th Annual Symposium on Combinatorial Pattern Matching

Bad Herrenalb, Germany, June 17–19, 2013

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Keynote Speakers

Moshe Lewenstein
Bar Ilan University, Israel

Orthogonal Range Searching and Text Indexing
Text indexing, the problem in which one desires to preprocess a (usually large) text for future (shorter) queries, has been researched ever since the suffix tree was invented in the early 70's. With textual data continuing to increase and with changes in the way it is accessed, new data structures and new algorithmic methods are continuously required. Therefore, text indexing is of utmost importance and is a very active research domain. Orthogonal range searching, classically associated with the computational geometry community, is one of the tools that has increasingly become important for various text indexing applications. Initially, in the mid 90's there were a couple of results recognizing this connection. In the last few years we have seen an increase in use of this method and are reaching a deeper understanding of the range searching uses for text indexing.

Gene Myers
MPI for Molecular Cell Biology and Genetics, Dresden, Germany

Discrete Methods for Image Analysis Applied to Molecular Biology
The field of image analysis and signal processing originally developed in the engineering community and is thus dominated by methods appealing to continuous mathematics. As a discrete mathematician recently entering this domain in the context of analyzing biological images, primarily from various forms of microscopy, I have found that discrete techniques involving trees and graphs better solve some segmentation and tracking problems than their continuous competitors. We illustrate this with three examples: component trees for adaptively segmenting nuclei in C. elegans 3D stacks, progress graph merging for segmenting cells in a 2D image of a fly wing, and shortest paths for segmenting and modeling individual neurons in a fly brain.