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汽車車牌傾斜圖像辨識系統之研究 = = A study of Tilt ...

郭頡生

汽車車牌傾斜圖像辨識系統之研究 = = A study of Tilt Vehicle License Plate Recognition System /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	汽車車牌傾斜圖像辨識系統之研究 =/ 郭頡生.
Reminder of title:	A study of Tilt Vehicle License Plate Recognition System /
remainder title:	A study of Tilt Vehicle License Plate Recognition System.
Author:	郭頡生
Published:	雲林縣 :國立虎尾科技大學 , : 民113.07.,
Description:	[10], 36面 :圖, 表 ; : 30公分.;
Notes:	指導教授: 陳世欣 .
Subject:	Convolutional Neural Networks. -
Online resource:	電子資源

汽車車牌傾斜圖像辨識系統之研究 = = A study of Tilt Vehicle License Plate Recognition System /
郭頡生

汽車車牌傾斜圖像辨識系統之研究 =A study of Tilt Vehicle License Plate Recognition System /A study of Tilt Vehicle License Plate Recognition System.郭頡生. - 初版. - 雲林縣 :國立虎尾科技大學 ,民113.07. - [10], 36面 :圖, 表 ;30公分.

指導教授: 陳世欣 .

碩士論文--國立虎尾科技大學自動化工程系碩士班.

含參考書目.

本論文研製一個汽車車牌辨識系統，使用雷登轉換矯正傾斜車牌，架構主要分為車牌定位、車牌傾斜矯正、字元切割以及字元辨識四個部分。車牌定位使用灰階化將影像通道數量由三減少至一，中值濾波過濾雜訊，邊緣偵測以凸顯影像中的垂直邊緣和使用大津二值化。接著使用形態學運算，經由區塊面積大小、寬高比例、區塊面積中的輪廓數量作為篩選，取得車牌候選位置。拍攝車牌可能形成車牌圖像傾斜情形，須做傾斜矯正以利後續辨識。車牌傾斜矯正包含水平傾斜矯正與垂直傾斜矯正，使用雷登轉換計算出車牌的傾斜角度，對傾斜車牌進行水平矯正，使用仿射轉換對水平矯正後的車牌做垂直修正，完成車牌傾斜矯正。字元切割包含水平投影切割與垂直投影切割，先將定位後車牌進行二值化，對其執行水平投影，切除中間車牌字元以外的區域，接著使用垂直投影，將同行的黑色像素數目做累加，依照字元間的間隔依序進行切割，完成字元切割。字元辨識是使用卷積神經網路對字元樣本進行訓練，最後將切割後的字元進行特徵比對完成字元辨識。.

(平裝)Subjects--Topical Terms:

1328816
Convolutional Neural Networks.

汽車車牌傾斜圖像辨識系統之研究 = = A study of Tilt Vehicle License Plate Recognition System /
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245 1 0 $a 汽車車牌傾斜圖像辨識系統之研究 = $b A study of Tilt Vehicle License Plate Recognition System / $c 郭頡生.
246 1 1 $a A study of Tilt Vehicle License Plate Recognition System.
250 $a 初版.
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300 $a [10], 36面 : $b 圖, 表 ; $c 30公分.
500 $a 指導教授: 陳世欣 .
500 $a 學年度: 112.
502 $a 碩士論文--國立虎尾科技大學自動化工程系碩士班.
504 $a 含參考書目.
520 3 $a 本論文研製一個汽車車牌辨識系統，使用雷登轉換矯正傾斜車牌，架構主要分為車牌定位、車牌傾斜矯正、字元切割以及字元辨識四個部分。車牌定位使用灰階化將影像通道數量由三減少至一，中值濾波過濾雜訊，邊緣偵測以凸顯影像中的垂直邊緣和使用大津二值化。接著使用形態學運算，經由區塊面積大小、寬高比例、區塊面積中的輪廓數量作為篩選，取得車牌候選位置。拍攝車牌可能形成車牌圖像傾斜情形，須做傾斜矯正以利後續辨識。車牌傾斜矯正包含水平傾斜矯正與垂直傾斜矯正，使用雷登轉換計算出車牌的傾斜角度，對傾斜車牌進行水平矯正，使用仿射轉換對水平矯正後的車牌做垂直修正，完成車牌傾斜矯正。字元切割包含水平投影切割與垂直投影切割，先將定位後車牌進行二值化，對其執行水平投影，切除中間車牌字元以外的區域，接著使用垂直投影，將同行的黑色像素數目做累加，依照字元間的間隔依序進行切割，完成字元切割。字元辨識是使用卷積神經網路對字元樣本進行訓練，最後將切割後的字元進行特徵比對完成字元辨識。.
520 3 $a This thesis develops a tilt vehicle license plate recognition system and uses Radon Transform method for tilted license plate. The architecture is mainly divided into four parts: license plate detection, license plate tilt correction, characters segmentation and characters recognition. License plate detection grayscales the image to reduce the number of image channels from three to one. The median filter filters out noise, and the edge detection method accentuates vertical edges in images. Otsu's method separates the image into two classes, foreground and background, further simplifying the image information. Then, morphology is used to connect specific blocks. The license plate candidate positions are obtained through block area size, width-to-height ratio, and the number of contours in the block area as filters. License plate tilt correction includes horizontal tilt correction and vertical tilt correction, The Radon Transform method is used to calculate the tilt angle of the tilted license plate, followed by performing horizontal correction. The affine transformation algorithm is used to perform vertical tilt correction on the horizontally corrected license plate, completing the tilt correction process. The character segmentation process includes horizontal projection segmentation and vertical projection segmentation. First, binarize the positioned license plate and perform horizontal projection on it to cut off the area other than the middle license plate characters. Then, use vertical projection to accumulate the number of black pixels in each column and cut them sequentially according to the intervals between characters to complete the character cutting. Character recognition uses convolutional neural networks to train character samples, and finally compares features of the segmented characters to complete the character recognition process..
563 $a (平裝)
650 # 4 $a Convolutional Neural Networks. $3 1328816
650 # 4 $a Affine Transform. $3 1450148
650 # 4 $a Radon Transform. $3 1450147
650 # 4 $a License Plate Recognition. $3 1434083
650 # 4 $a 卷積神經網路. $3 1127424
650 # 4 $a 仿射轉換. $3 1450146
650 # 4 $a 雷登轉換. $3 1450145
650 # 4 $a 車牌辨識. $3 997603
856 7 # $u https://handle.ncl.edu.tw/11296/m578qp $z 電子資源 $2 http