Data Processing and Information Technology with an Optimal Matching Model Used to Reconstruct Broken Pieces

Wei Gu

doi:10.4028/www.scientific.net/AMR.1014.421

Paper Titles

Applied-Information Technology in the Internet of Things in the Construction of File Security System in Colleges and Universities
p.399

Data processing with an Improved Hybrid Optimization Algorithm Base on PSO-GA
p.404

Digital Signal Modulation with Recognition Algorithm to Identify the Ability to Optimize the Wavelet
p.413

Hidden Information Technology and Data Processing in Drowsiness Detection in Normal Adults Based on Pulse Signal and ECG Detection in Normal Adults Based on Pulse Signal and ECG
p.417

Data Processing and Information Technology with an Optimal Matching Model Used to Reconstruct Broken Pieces
p.421

Research on Applied Technology with Variable Parameter Iteration Method Research of Two-Dimensional NMR Inversion
p.424

Research on Information Technology with the Consideration of Computer Professional Education in Big Data Era
p.429

Exploitation and Application of Information System in Interpretation Training
p.434

Empirical Study on the Marketing Strategy and Effect on the Platform of Social Media: Taking Tencent Microblog as Example
p.438

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1014Data Processing and Information Technology with an...

Data Processing and Information Technology with an Optimal Matching Model Used to Reconstruct Broken Pieces

Abstract:

Splicing broken files played an important role in judicial evidence reconstruction, historical documents repair and military information obtainment and other fields. In this paper, we analyzed the reconstruction of broken pieces, which was modeled as an optimal matching problem. The Hamming distance was used as the matching degree of edge splicing. Firstly, we use the iterative threshold selection method to determine the gray scale threshold. Secondly, we chose the minimum summation of and as objective function. Then we use clustering analysis to classify the pieces into different lines, and selected the top and bottom position of the first line on each piece as decision variables. Finally, we took word width and column spacing into consideration and used row matching algorithm to splice lines into a complete image.

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Periodical:

Advanced Materials Research (Volume 1014)

Pages:

421-423

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1014.421

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Online since:

July 2014

Authors:

Wei Gu*

Keywords:

Clustering Analysis, Gray Scale Selection, Hamming Distance, Iterative Threshold Selection Method

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