Polygonal shape recognition using string-matching techniques

String matching is one of the most typical problems in computer science. Previous studies mainly focused on accurate string matching problem. However, with the rapid development of the computer and Internet as well as the continuously rising of new issues, people find that it has very important theoretical value and practical meaning to research and design efficient approximate string matching algorithms. Approximate string matching is also called string matching that allows errors, which mainly aims to find the pattern string in the text and database and allows k differences between the pattern string and its occurring forms in the text. For the problem of approximate string matching, though a number of algorithms have been proposed, there are fewer studies which focus on large size of alphabet . Most of experts are interested in small or middle size of alphabet . For large size of , especially for Chinese characters and Asian phonetics, there are fewer efficient algorithms. For the above reasons, this paper focuses on the approximate Chinese strings matching problem based on the pinyin input method.

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Correcting and Standardizing Crude Drug Names in Traditional Medicine Formulae by Ensemble of String Matching Techniques

Intelligent Computing Theories and Methodologies - Lecture Notes in Computer Science ◽

10.1007/978-3-319-22186-1_24 ◽

2015 ◽

pp. 237-247 ◽

Cited By ~ 2

Author(s):

Duangkamol Pakdeesattayapong ◽

Verayuth Lertnattee

Keyword(s):

Traditional Medicine ◽

String Matching ◽

Crude Drug ◽

Drug Names ◽

Matching Techniques

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MODEL-GUIDED ATTRIBUTED STRING MATCHING BY SPLIT-AND-MERGE FOR SHAPE RECOGNITION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001489000140 ◽

1989 ◽

Vol 03 (02) ◽

pp. 159-179 ◽

Cited By ~ 26

Author(s):

YIH-TAY TSAY ◽

WEN-HSIANG TSAI

Keyword(s):

Shape Matching ◽

Pattern Analysis ◽

Shape Recognition ◽

String Matching ◽

General Shape ◽

Edit Operation ◽

Structural Pattern ◽

Split Operation ◽

Split And Merge ◽

Shape Distance

Due to noise and distortion, segmentation uncertainty is a key problem in structural pattern analysis. In this paper we propose the use of the split operation for shape recognition by attributed string matching. After illustrating the disadvantage of attributed string matching using the merge operation, the split operation is proposed. Under the guidance of the model shape, an input shape can be reapproximated, using the split operation, into a new attributed string representation. By combining the split and the merge operations for shape matching it is unnecessary to apply any type of edit operation to a model shape. This makes the distance between the input shape and the model shape more meaningful and stable, and improves recognition results. An algorithm for attributed string matching by split-and-merge is proposed. To eliminate the effect of the numbers of primitives in the model shape on the shape distance, shape recognition based on a similarity measure is also proposed. Good experimental results prove the feasibility of the proposed approach for general shape recognition.

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Association of Patient-Centered Medical Home designation and quality indicators within HRSA-funded community health center delivery sites

BMC Health Services Research ◽

10.1186/s12913-020-05826-x ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Nathaniel Bell ◽

Rebecca Wilkerson ◽

Kathy Mayfield-Smith ◽

Ana Lòpez-De Fede

Keyword(s):

Community Health ◽

Medical Home ◽

Clinical Performance ◽

String Matching ◽

Care Quality ◽

Approximate String Matching ◽

Patient Centered Medical Home ◽

Patient Centered ◽

Matching Algorithm ◽

Matching Techniques

Abstract Background Patient-Centered Medical Home (PCMH) adoption is an important strategy to help improve primary care quality within Health Resources and Service Administration (HRSA) community health centers (CHC), but evidence of its effect thus far remains mixed. A limitation of previous evaluations has been the inability to account for the proportion of CHC delivery sites that are designated medical homes. Methods Retrospective cross-sectional study using HRSA Uniform Data System (UDS) and certification files from the National Committee for Quality Assurance (NCQA) and the Joint Commission (JC). Datasets were linked through geocoding and an approximate string-matching algorithm. Predicted probability scores were regressed onto 11 clinical performance measures using 10% increments in site-level designation using beta logistic regression. Results The geocoding and approximate string-matching algorithm identified 2615 of the 6851 (41.8%) delivery sites included in the analyses as having been designated through the NCQA and/or JC. In total, 74.7% (n = 777) of the 1039 CHCs that met the inclusion criteria for the analysis managed at least one NCQA- and/or JC-designated site. A proportional increase in site-level designation showed a positive association with adherence scores for the majority of all indicators, but primarily among CHCs that designated at least 50% of its delivery sites. Once this threshold was achieved, there was a stepwise percentage point increase in adherence scores, ranging from 1.9 to 11.8% improvement, depending on the measure. Conclusion Geocoding and approximate string-matching techniques offer a more reliable and nuanced approach for monitoring the association between site-level PCMH designation and clinical performance within HRSA’s CHC delivery sites. Our findings suggest that transformation does in fact matter, but that it may not appear until half of the delivery sites become designated. There also appears to be a continued stepwise increase in adherence scores once this threshold is achieved.

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Extended Attributed String Matching for Shape Recognition

Computer Vision and Image Understanding ◽

10.1006/cviu.1998.0599 ◽

1998 ◽

Vol 70 (1) ◽

pp. 36-50 ◽

Cited By ~ 19

Author(s):

S.W. Chen ◽

S.T. Tung ◽

C.Y. Fang ◽

Shen Cherng ◽

Anil K. Jain

Keyword(s):

Shape Recognition ◽

String Matching

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