| 000 | 04082nla2a2200433 4500 | ||
|---|---|---|---|
| 001 | 641421 | ||
| 005 | 20231030040427.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\6338 | ||
| 035 | _aRU\TPU\network\6336 | ||
| 090 | _a641421 | ||
| 100 | _a20150519a2015 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 105 | _ay z 100zy | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aApplication of Convolutional Neural Networks for Automatic Number Plate Recognition on Complex Background Images _fA. A. Druki, Yu. A. Bolotova, V. G. Spitsyn |
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| 203 |
_aText _celectronic |
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| 225 | 1 | _aImage and Signal Processing, Recognition, Information Processing and Applied Technologies | |
| 300 | _aTitle screen | ||
| 330 | _aThe relevance of this study is stipulated by the necessity of designing techniques, algorithms, and programs improving the efficiency of automatic number plate recognition (ANPR) on images with complex backgrounds.Purpose: The aim of this work is to improve the efficiency of automatic number plate recognition on images with complex backgrounds using methods, algorithms, and programs invariant to affine and projective transformations.Design/methodology: Such techniques as artificial intelligence, pattern identification and recognition, the theory of artificial neural networks (ANN), convolutional neural networks (CNN), evolutionary algorithms, mathematical modeling, the probability theory and mathematical statistics were applied via Visual Studio and MatLab software.Findings: The software is developed allowing the automatic number plate recognition on complex background images. The convolutional neural network comprising seven layers is suggested to identify the plate localization, i.e. finding and isolating the plate on the picture. The pixel intensity histogram-based algorithm was used for character segmentation or finding individual characters on the plates. The convolutional neural network comprising six layers is designed to recognize characters. The suggested software system allows automatic number plate recognition at large angles of inclinations and rather a high speed. | ||
| 333 | _aРежим доступа: по договору с организацией-держателем ресурса | ||
| 461 | 0 |
_0(RuTPU)RU\TPU\network\5920 _tApplied Mechanics and Materials _oScientific Journal |
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| 463 | 0 |
_0(RuTPU)RU\TPU\network\6028 _tVol. 756 : Mechanical Engineering, Automation and Control Systems (MEACS2014) _oInternational Conference, 16‐18 October, 2014, Tomsk, Russia _o[proceedings] _fNational Research Tomsk Polytechnic University (TPU) _v[P. 695-703] _d2015 |
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| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _aискусственный интеллект | |
| 610 | 1 | _aраспознавание символов | |
| 610 | 1 | _aобработка | |
| 610 | 1 | _aизображения | |
| 610 | 1 | _aгистограммы | |
| 610 | 1 | _aнейтронные сети | |
| 700 | 1 |
_aDruki _bA. A. _cspecialist in the field of informatics and computer technology _cassistant of Tomsk Polytechnic University, engineer _f1985- _gAleksey Alekseevich _2stltpush _3(RuTPU)RU\TPU\pers\34610 |
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| 701 | 1 |
_aBolotova _bYu. A. _cspecialist in the field of informatics and computer technology _cPostgraduate of Tomsk Polytechnic University _f1986- _gYuliya Aleksandrovna _2stltpush _3(RuTPU)RU\TPU\pers\33458 |
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| 701 | 1 |
_aSpitsyn _bV. G. _cspecialist in the field of informatics and computer technology _cProfessor of Tomsk Polytechnic University, Doctor of technical sciences _f1948- _gVladimir Grigorievich _2stltpush _3(RuTPU)RU\TPU\pers\33492 |
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| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет (ТПУ) _bИнститут кибернетики (ИК) _bКафедра вычислительной техники (ВТ) _h126 _2stltpush _3(RuTPU)RU\TPU\col\18699 |
| 801 | 2 |
_aRU _b63413507 _c20161229 _gRCR |
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| 856 | 4 | _uhttp://dx.doi.org/10.4028/www.scientific.net/AMM.756.695 | |
| 942 | _cCF | ||