東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Automatic License Plate Recognition ...

Fu, Yuanxi.

Linked to FindBook

Google Book

Amazon

博客來

Automatic License Plate Recognition Using Neural Network and Signal Processing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automatic License Plate Recognition Using Neural Network and Signal Processing./
Author:	Fu, Yuanxi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	44 p.
Notes:	Source: Masters Abstracts International, Volume: 80-12.
Contained By:	Masters Abstracts International80-12.
Subject:	Computer Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13808977
ISBN:	9781392170779

Automatic License Plate Recognition Using Neural Network and Signal Processing.
Fu, Yuanxi.

Automatic License Plate Recognition Using Neural Network and Signal Processing. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 44 p.

Source: Masters Abstracts International, Volume: 80-12.

Thesis (M.S.)--University of California, Riverside, 2019.

This item must not be added to any third party search indexes.

Automatic Plate Recognition plays an important roll in intelligent transportation systems. However, most license plate recognition methods work under restricted conditions like slow speed and good illumination. That is a restriction on industrial application. In this thesis, the constraints are relaxed by vanished points distortion-recovery method and denoising method. This thesis implements a license plate recognition method by morphological edge detection method and convolution neural network recognition method. The thesis is constructed contributes to several papers optimization methods. The proposed approach can be trained for recognition of country-specific license plates. More than 500 images are collected for training and over 300 images are collected for recognition test. This paper achieves 97.05% on license plate recognition for detecting total characters and numbers of the license plates. License plate recognition consists three parts, pre-processing image, locating license plate and identifying license numbers and characters. License plate location is important to obtain license images and plays a key role in identifying plates. The plate recognition has two major steps, character separation and identification. In this paper, machine learning method is applied for license plate recognition.

ISBN: 9781392170779Subjects--Topical Terms:

1567821
Computer Engineering.

Automatic License Plate Recognition Using Neural Network and Signal Processing.
LDR:02472nmm a2200349 4500 001 2209052
005 20191025102641.5
008 201008s2019 ||||||||||||||||| ||eng d
020 $a 9781392170779
035 $a (MiAaPQ)AAI13808977
035 $a (MiAaPQ)ucr:13706
035 $a AAI13808977
040 $a MiAaPQ $c MiAaPQ
100 1 $a Fu, Yuanxi. $3 3436132
245 1 0 $a Automatic License Plate Recognition Using Neural Network and Signal Processing.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 44 p.
500 $a Source: Masters Abstracts International, Volume: 80-12.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Hua, Yingbo.
502 $a Thesis (M.S.)--University of California, Riverside, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Automatic Plate Recognition plays an important roll in intelligent transportation systems. However, most license plate recognition methods work under restricted conditions like slow speed and good illumination. That is a restriction on industrial application. In this thesis, the constraints are relaxed by vanished points distortion-recovery method and denoising method. This thesis implements a license plate recognition method by morphological edge detection method and convolution neural network recognition method. The thesis is constructed contributes to several papers optimization methods. The proposed approach can be trained for recognition of country-specific license plates. More than 500 images are collected for training and over 300 images are collected for recognition test. This paper achieves 97.05% on license plate recognition for detecting total characters and numbers of the license plates. License plate recognition consists three parts, pre-processing image, locating license plate and identifying license numbers and characters. License plate location is important to obtain license images and plays a key role in identifying plates. The plate recognition has two major steps, character separation and identification. In this paper, machine learning method is applied for license plate recognition.
590 $a School code: 0032.
650 4 $a Computer Engineering. $3 1567821
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer science. $3 523869
690 $a 0464
690 $a 0544
690 $a 0984
710 2 $a University of California, Riverside. $b Electrical Engineering. $3 1669528
773 0 $t Masters Abstracts International $g 80-12.
790 $a 0032
791 $a M.S.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13808977