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Fingerprints Matching Using Bozorth3 Algorithm And Parallel Computation On Nvidia Compute Unified Device Architecture
Author

Sri Supatmi, S.Kom.,M.T., D.Sc Irfan Dwiguna Sumitra, S.Kom., M.Kom. Ph.D

Abstrak
This paper studied fingerprint matching employing Bozorth3 Algorithm for matching fingerprint and parallel computation employing NVIDIA Compute Unified Device Architecture (NVIDIA CUDA). The objective of this study obtains the percentage and time processing of matching fingerprints. In this study, the fingerprint matching is done with parallel computing is applied to the GPU (Graphics Processing Unit). GPU device used in this study is the CUDA (Compute Unified Device Architecture), which is an Application Programming Interface (API) developed by NVIDIA. The development of applications with fingerprint matching serial computing on CPU and parallel computing on GPU can be applied to the CUDA API. The results from this study can be found in the performance process on the CPU and GPU. The results of this research are the process on CUDA execution time is better than the execution time on the CPU, the process is done at both the computation is to find a match in the fingerprint value.
Nama Prosiding

IOP Conference Series: Materials Science and Engineering
Volume 879 Nomor 2

URL

https://iopscience.iop.org/article/10.1088/1757-899X/879/1/012109

DOI

https://doi.org/10.1088/1757-899X/879/1/012109

Maximally Stable Extremal Regions And Nave Bayes To Detect Scene Text
Author

Dr. Ednawati Rainarli, S.Si., M.Si.

Abstrak
This study examines the performance of Maximally Stable Extremal Regions (MSER) and Naïve Bayes in detecting scene text. The variance of types and sizes of fonts, uneven lighting conditions, the text orientation, a complex background, occlusion, and the presence of objects that resemble text, make the scene text detection is quite challenging. The initial stage of the detection process is to use MSER to get the candidate characters in the image. The validation process of the candidate character uses the Naïve Bayes classifier, which we trained using char74k and CIFAR10 data sets. The classification process used HOG as the extracted features. The system validates the candidates by comparing the Naïve Bayes probability value with the specified threshold value. By using 100 images from ICDAR 2015, the research obtains a 50% reduction of the candidate with an accuracy increase of 8% for Naïve Bayes using threshold values. The result shows that Naïve Bayes with the thresholding value is better than the usual Naïve Bayes classification in selecting candidates.
Nama Prosiding

IOP Conference Series: Materials Science and Engineering
Volume 879 Nomor 1

URL

https://iopscience.iop.org/article/10.1088/1757-899X/879/1/012106/meta

DOI

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