Pattern matching for spatial point sets

Cardoze, David E.; Schulman, Leonard J.

doi:10.1109/sfcs.1998.743439

Cited by 44 publications

(33 citation statements)

References 25 publications

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“…We do not seek to minimize δ but rather adopt an acceptable threshold for δ. The threshold is relatively small compared to the average inter-point distances in S. In fact, this sort of problem was categorized as a "Nearly Exact" point matching problem in [6]. Given the parameters α and δ, to obtain a proper transformation T, we need to compute the values of the four unknown parameters S x, S y, T x and Ty.…”

Section: Identifying Intersection Points From Street Mapsmentioning

confidence: 99%

“…Road vector data covering all of the United States is available from the U.S. Census Bureau. 6 A general problem in combining geospatial data from different sources is that they rarely align. There are a variety of reasons for this problem, but the most common one is that the latest products are collected with higher accuracy and improved processing techniques.…”

Section: Introductionmentioning

confidence: 99%

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Automatically and Accurately Conflating Raster Maps with Orthoimagery

Chen¹,

Knoblock

Shahabi

2007

Geoinformatica

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Recent growth of geospatial information online has made it possible to access various maps and orthoimagery. Conflating these maps and imagery can create images that combine the visual appeal of imagery with the attribution information from maps. The existing systems require human intervention to conflate maps with imagery. We present a novel approach that utilizes vector datasets as "glue" to automatically conflate street maps with imagery. First, our approach extracts road intersections from imagery and maps as control points. Then, it aligns the two point sets by computing the matched point pattern. Finally, it aligns maps with imagery based on the matched pattern. The experiments show that our approach can conflate various maps with imagery, such that in our experiments on TIGER-maps covering part of St. Louis county, MO, 85.2% of the conflated map roads are within 10.8 m from the actual roads compared to 51.7% for the original and georeferenced TIGER-map roads.

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Section: Identifying Intersection Points From Street Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatically and Accurately Conflating Raster Maps with Orthoimagery

Chen¹,

Knoblock

Shahabi

2007

Geoinformatica

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“…It can be shown (using similar ideas as in [6]) that if a translation j does not result in a match between p and t, it will remain a mismatch between p ′ and t ′ with constant probability. Therefore, all possible mismatches will be detected with high probability by performing O(log M ) mappings modulo a random prime.…”

Section: Matching Horizontal Segments Under Vertical Translationmentioning

confidence: 99%

Pattern Matching for Sets of Segments

2004

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In this paper we present algorithms for a number of problems in geometric pattern matching where the input consist of a collections of segments in the plane. Our work consists of two main parts. In the first, we address problems and measures that relate to collections of orthogonal line segments in the plane. Such collections arise naturally from problems in mapping buildings and robot exploration.We propose a new measure of segment similarity called a coverage measure, and present efficient algorithms for maximising this measure between sets of axis-parallel segments under translations. Our algorithms run in time O(n 3 polylog n) in the general case, and run in time O(n 2 polylog n) for the case when all segments are horizontal. In addition, we show that when restricted to translations that are only vertical, the Hausdorff distance between two sets of horizontal segments can be computed in time roughly O(n 3/2 polylog n). These algorithms form significant improvements over the general algorithm of Chew et al. that takes time O(n 4 log 2 n).In the second part of this paper we address the problem of matching polygonal chains. We study the well known Fréchet distance , and present the first algorithm for computing the Fréchet distance under general translations. Our methods also yield algorithms for computing a generalization of the Fréchet distance, and we also present a simple approximation algorithm for the Fréchet distance that runs in time O(n 2 polylog n).

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“…The authors consider matchings as an approach for the problem of matching a point set A with a point set B, where A must be moved in some way to coincide as much as possible with B or one of its subsets. This is a fundamental problem in pattern recognition [5,7,8,[10][11][12][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Matching Points with Things

Aloupis

Cardinal

Collette

et al. 2010

LATIN 2010: Theoretical Informatics

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Abstract. Given an ordered set of points and an ordered set of geometric objects in the plane, we are interested in finding a non-crossing matching between point-object pairs. We show that when the objects we match the points to are finite point sets, the problem is NP-complete in general, and polynomial when the objects are on a line or when their number is at most 2. When the objects are line segments, we show that the problem is NP-complete in general, and polynomial when the segments form a convex polygon or are all on a line. Finally, for objects that are straight lines, we show that the problem of finding a min-max non-crossing matching is NP-complete.

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Pattern matching for spatial point sets

Cited by 44 publications

References 25 publications

Automatically and Accurately Conflating Raster Maps with Orthoimagery

Automatically and Accurately Conflating Raster Maps with Orthoimagery

Pattern Matching for Sets of Segments

Matching Points with Things

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