模糊車牌字元預測使用超解析度影像重建技術

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鄧名杉(Ming-Sang Deng) 查詢紙本館藏

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資訊工程學系

論文名稱

模糊車牌字元預測使用超解析度影像重建技術
(Blur License Plate Character Prediction Using Super-Resolution Based Image Reconstruction Technique)

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摘要(中)

車牌影像的分析與應用是長久以來電腦視覺領域中一個重要的課題，在近年來，以此技術為基礎的應用正在蓬勃發展當中。舉例來說：道路監控、贓車追蹤、出入口監測等等，都是典型的應用。本篇論文除了一般狀況的車牌影像之外，更特別專注於透過超解析度技術來處理模糊車牌的案例。
本篇論文主要目標是設計一個能夠克服低解析度、光影變化，以及因為車距過遠所造成的車牌過小的狀況之影像還原系統。利用LBP特徵與RBF類神經網路進行特徵轉換，搭配影像分析演算法來進行模糊車牌的分析，接著利用兩階段式的影像還原，一步步將低解析影像還原成高解析影像，而每一次的還原都會產生一個新的影像，每一個新影像都會進行分析與投票。最終再依據投票結果進行影像重建的步驟。這個做法的目的在於透過一次又一次的分析與還原的過程中，強化原本因為模糊而弱化的影像特徵。
在實驗方面分成三大部分。第一部分採用模糊但肉眼可辨識的車牌影像，其目標為還原出正確且易讀的車牌。第二與第三部分則採用模糊且肉眼無法辨識的車牌影像，並透過「候選名單」的排名，來進行準確度的分析。該候選名單可以用來做為模糊車牌解答之參考依據，藉以降低模糊車牌比對上的搜尋範圍。實驗結果顯示，本論文所提出的方法對於模糊車牌有很高的還原成功率，而即便在非常模糊以至於肉眼無法分辨的車牌影像上，仍然可以有很好的辨識成效。

摘要(英)

The research on vehicle license plate is an important issue in computer vision. In recent years, applications based on this technology are getting popular. For example, entrance monitoring system, road surveillance, suspicious vehicle investigation…etc. In our work, we are not only dealing with the normal case but also focusing on the blurred plate images using super-resolution technique. The purpose of this thesis is to design a vehicle license plate analysis and image reconstruction system that can overcome the following cases: blurred images, images with variation illumination intensity, and tiny target images.
In this thesis, we adopt LBP as image feature and RBF neural network to perform feature translation in the first stage. After that, an algorithm for analyzing the image and performing the image reconstruction is proposed in the second stage. In image reconstruction stage, two-layer architecture is applied. In the first step, image reconstruction is performed recursively to obtain better reconstruction result. A new image will be generated each time and each new image will go through analysis and voting procedure recursively. In the second step, image reconstruction is further manipulated according to the voting result. The purpose of image reconstruction is to intensify the weak clue in the blurred image recursively.
Three different experiments were conducted to verify the validity of our proposed method. The images in the first experiment are blurred but can be identified by human. We can apply the proposed method to reconstruct the original blurred images into clear and correct license plate images. The images of the second and third experiments are very blurred and cannot be identified by human. Here, we utilize the candidate ranking list for analyzing the accuracy. This list can help police to reduce the search space when performing the blurred license plate image matching for criminal investigation.

關鍵字(中)

★ 車牌切割
★ 車牌辨識
★ 超解析度
★ 影像還原

關鍵字(英)

★ Vehicle license plate segmentation
★ vehicle license plate recognition
★ super-resolution
★ image reconstruction

論文目次

目錄
摘要 i
致謝 iv
目錄 v
圖目錄 vii
表目錄 viii
演算法目錄 viii
第一章緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 系統架構 3
1.4 論文架構 4
第二章文獻探討 5
2.1 車牌偵測 5
(1) 顏色為基礎的偵測(Color-based) 6
(2) 邊緣特徵為基礎的偵測(Edge-based) 6
(3) 字元特徵為基礎的偵測(Texture-based) 6
(4) 其他 7
2.2字元分割 7
(1) 利用像素的連通性 8
(2) 利用投影分析 8
(3) 利用預備知識(Prior Knowledge) 8
(4) 利用多特徵之結合 8
2.3 字元辨識 9
(1) 使用原始數據 10
(2)使用特徵擷取 10
2.4 超解析度技術 10
(1) 頻率域分析法(Frequency Domain) 11
(2) 確定性正則化法(Deterministic Regularization) 11
(3)機率統計法(Stochastic Methods) 11
(4)以學習為基礎法(Learning-based Methods) 11
2.5 模糊車牌探討 12
第三章相關研究 13
3.1 車牌偵測 13
3.1.1 自適應增強演算法(Adaboost Algorithm) 13
3.1.2 單一迴積特徵 16
3.1.3 層級分類器(Cascade classifier) 16
3.2 字元辨識之特徵-LBP(Local Binary Pattern) 17
3.2.1 LBP(Local Binary Pattern) 17
3.2.2 統一模式(uniform patterns) 18
3.3 RBF類神經網路 18
3.4 基因演算法 19
第四章系統流程與演算法 21
4.1 系統流程介紹 21
4.2 字元切割 22
4.2.1 車牌ROI擷取 23
4.2.2 依車牌規則的切割法 23
(1)第一種類型切割法 24
(2)第二種類型切割法 24
4.3 特徵擷取與分析 24
4.3.1 特徵擷取 25
4.3.2 類神經網路的訓練與應用 26
(1)資料收集 26
(2)訓練 27
4.3.3 影像分析 29
4.4 影像重建 29
4.4.1 概念闡述 29
4.4.2 第一階段影像還原 32
(1)分配權重 32
(2)計算區塊相似度 32
(3)影像還原 32
(4)影像分析與投票機制 33
4.4.3 第二階段影像還原 33
(1)高清影像還原 34
(2)評估準則 34
第五章實驗結果與分析 36
第六章結論與未來研究方向 53
參考文獻 55

參考文獻

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指導教授

范國清、莊啟宏(Kuo-Chin Fan Chi-Hung Chuang)

審核日期

2014-7-29

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