Shape recognition using attributed string matching with polygon vertices as the primitives

Kaygin, Serkan; Bulut, Mehmet

doi:10.1016/s0167-8655(01)00111-8

Cited by 20 publications

(10 citation statements)

References 14 publications

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“…These algorithms take into account both the order and location of an object's vertices and match the contours of an object pair by comparing their vertex strings [48]. Nevertheless, these methods, as in those described above, present some disadvantages, which in this case, are related mainly to the comparison between vertex strings, which belong to objects with relatively different contours.…”

Section: Intra-elements Matching (Vertex-to-vertex)mentioning

confidence: 99%

A Polygon and Point-Based Approach to Matching Geospatial Features

Ruiz-Lendínez

Ureña-Cámara

López

2017

IJGI

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A methodology for matching bidimensional entities is presented in this paper. The matching is proposed for both area and point features extracted from geographical databases. The procedure used to obtain homologous entities is achieved in a two-step process: The first matching, polygon to polygon matching (inter-element matching), is obtained by means of a genetic algorithm that allows the classifying of area features from two geographical databases. After this, we apply a point to point matching (intra-element matching) based on the comparison of changes in their turning functions. This study shows that genetic algorithms are suitable for matching polygon features even if these features are quite different. Our results show up to 40% of matched polygons with differences in geometrical attributes. With regards to point matching, the vertex from homologous polygons, the function and threshold values proposed in this paper show a useful method for obtaining precise vertex matching.

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Section: Intra-elements Matching (Vertex-to-vertex)mentioning

confidence: 99%

A Polygon and Point-Based Approach to Matching Geospatial Features

Ruiz-Lendínez

Ureña-Cámara

López

2017

IJGI

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“…The costs for these operations are derived from the deformation of the involved line segments in terms of the angle and length changes. The optimal operation sequence with the minimum total cost is obtained by means of a dynamic programming optimization technique [9,10]. The corresponding point pairs are then considered point pairs for which the snapping operation is chosen in a sequence.…”

Section: Open Accessmentioning

confidence: 99%

Local Edge Matching for Seamless Adjacent Spatial Datasets with Sequence Alignment

Huh¹

2015

IJGI

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This study proposes a local edge matching method with a sequence alignment technique for adjacent spatial datasets. By assuming that the common boundary edges of the datasets are point strings, the proposed method obtains the sequence for point edit operations to align the edges by using the string matching algorithm with the following operations: (1) snapping two points from each string to their average position, (2) removing a point from one string and (3) removing a point from the other string. The costs for these operations are derived from the deformation of the involved line segments in terms of the angle and length changes. The corresponding point pairs are then considered point pairs for which the snapping operation is chosen in a sequence. Based on these pairs, a border area of adjacent spatial datasets can be partitioned into sub-border areas where distinctive matching and alignment processes can be performed.

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“…This has been studied as a contour matching. Among the contour-matching algorithms, the most appropriate is the vertices-attributed-string-matching (VASM) algorithm of Kaygin and Bulut (2002) because it matches the contours of an object pair by computing an optimal edit sequence for converting one vertex string into the other vertex string through the perspective of their vertices.…”

Section: Introductionmentioning

confidence: 99%

“…Third, the VASM algorithm obtains an optimal sequence of edit operations based on the cost functions of each edit operation. Kaygin and Bulut (2002) calculated these costs with a simple weighted summation of angle and length differences caused by the edit operations. They applied the algorithm on polygons with simple shapes and obtained satisfactory results.…”

Section: Introductionmentioning

confidence: 99%