H.J. Wolfson and I. Rigoutsos. Geometric hashing: An introduction. IEEE Computational Science & Engineering, pages 10--21, Oct-Dec 1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....alignment of proteins [7] The first set performs structural alignment directly at the level atoms. The second group of algorithms first uses the SSEs (Secondary Structure Elements) to carry out an approximate alignment and then uses atoms. The final group of algorithms uses geometric hashing [22]. The simplest algorithm for structural alignment [9] uses dynamic programming to find the optimal correspondence. The DALI algorithm [10] uses distance matrices to align proteins. The CE algorithm [18] performs a combinatorial extension of aligned fragment pairs. The Double Dynamic Programming ....

H.J. Wolfson and I. Rigoutsos. Geometric hashing: An introduction. IEEE Computational Science & Engineering, pages 10--21, Oct-Dec 1997. 10

....components, for discovering identical connected component level features in the model has a complexity of ### # #, where # is the number of connected components in the given model. In most engineering models, # tends to be large. We have accelerated the algorithm by using geometric hashing [12, 22] of components. To achieve hashing of components such that candidates for detailed geometric matching are put into the same bucket, we need some invariant descriptors. In our implementation, we carry out hashing using the number of vertices and triangles as the key, and before performing detailed ....

Haim J. Wolfson and Isidore Rigoutsos. Geometric Hashing: An Introduction. IEEE Computational Science & Engineering, pages 10-- 21, October-December 1997.

....correct and efficient solutions to geometric problems. First some related work is mentioned in the next subsection. 1. 1 Related work Matching has been approached in a number of ways, including tree pruning [55] the generalized Hough transform [8] or pose clustering [51] geometric hashing [59], the alignment method [27] statistics [40] deformable templates [50] relaxation labeling [44] Fourier descriptors [35] wavelet transform [31] curvature scale space [36] and neural networks [21] The following subsections treat a few methods in more detail. They are based on shape ....

....but often performs linear in practice. 5 Algorithms In the previous section, algorithms were mentioned along with the description of the measure, when the algorithm is specific for that measure. This section mentions a few algorithms that are more general. 5. 1 Voting schemes Geometric hashing [33, 59] is a method that determines if there is a transformed subset of the query point set that matches a subset of a target point set. The method first constructs a single hash table for all target point sets together. It is described here for 2D. Each point is represented as # # # ### # # # # ##### ....

H. Wolfson and I. Rigoutsos. Geometric hashing: an overview. IEEE Computational Science & Engineering, pages 10--21, October-December 1997.

....not very robust for errors: the success of the technique depends on the correct extraction of the graphs from the input. Another limitation of graph matching is a lack of discernment: large classes of patterns share the same graph. 10 CHAPTER 1. INTRODUCTION Geometric hashing Geometric hashing [127, 128] is another class of correspondence methods. In geometric hashing, the geometric primitives that make up a pattern are used to generate a normalised description of the pattern as a whole. For example, for finite point sets in the plane, an a#ne invariant description is generated when the point set ....

H. J. Wolfson and I. Rigoutsos. Geometric hashing: An overview. IEEE Computational Science & Engineering, 4(4), October 1997.

....2: Fingerprint matching. Figure 3: Query hieroglyph (left) and hieroglyphs retrieved from database, from [VV99] 2 Approaches Matching has been approached in a number of ways, including tree pruning [Ume93] the generalized Hough transform or pose clustering [Bal81] Sto87] geometric hashing [WR97] the alignment method [HU87] statistics [Sma96] deformable templates [SP95] relaxation labeling [RR80] Fourier descriptors [Lon98] wavelet transform [JFS95] curvature scale space [MAK96] and neural networks [Gol95] Without being complete, in the following subsections we will describe and ....

....(see Section 5.1) or tangent space representation [LL99] 2.3 Voting schemes The voting schemes discussed here generally work on so called interest points. For the purpose of visual information systems, such points are for example corner points detected in images. Geometric hashing [LW88, WR97] is a method that determines if there is a transformed subset of the query point set that matches a subset of a target point set. The method rst constructs a single hash table for all target point sets together. Each point is represented as e 0 (e 1 e 0 ) e 2 e 0 ) for some xed choice ....

Haim Wolfson and Isidore Rigoutsos. Geometric hashing: an overview. IEEE Computational Science & Engineering, pages 10-21, October-December 1997.

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H.J. Wolfson and I. Rigoutsos. Geometric hashing: An introduction. IEEE Computational Science & Engineering, pages 10--21, Oct-Dec 1997.

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H.J. Wolfson and I. Rigoutsos. Geometric hashing: An introduction. IEEE Computational Science & Engineering, pages 10--21, Oct-Dec 1997.

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Haim J. Wolfson and Isidore Rigoutsos. Geometric hashing: An overview. IEEE Computational Science & Engineering, 4(4):10--21, 1997.

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H.J. Wolfson and I. Rigoutsos. Geometric hashing: An introduction. IEEE Computational Science & Engineering, pages 10--21, Oct-Dec 1997. 11

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