Rotation Invariant Template Matching

In rotation invariant template matching one wants to find nding from the given input image the locations that are similar to the given pattern template, so that the pattern may have any orientation. Template matching is an old but important problem in computer vision and pattern recognition, and thus there have also been many attempts to solve it. Most of the most successful solutions so far come from the signal processing community, based on fast computation of cross correlation or correlation coefficient. The existing combinatorial approaches have ignored the template rotations. This thesis fills in this gap by presenting the rst rotation invariant combinatorial template matching algorithms. The thesi

Revisiting Multiple Pattern Matching

We consider the classical exact multiple string matching problem. The proposed solution is based on a combination of a few ideas: using q-grams instead of single characters, pattern superimposition, bit-parallelism and alphabet size reduction. We discuss the pros and cons of various alternatives to achieve the possibly best combination of techniques. The main contribution of this paper are different alphabet mapping methods that allow to reduce memory requirements and use larger q-grams. The experimental results show that the presented algorithm is competitive in most practical cases. One of the tests shows also that tailoring our scheme to search over a byte-encoded text results in speedups in comparison to searching over a plain text