臺灣博碩士論文加值系統

在21世紀即將來臨之際，科技的進步是遠遠的超越了我們想像。雖然為我們帶來了相當多的便利，可是相對的也帶來了許多問題。以交通為例，大量的車輛雖然使得人們大大的節省了許多時間，可是隨之而來的卻是交通擁擠與事故發生的頻繁。所以我們必須善用科技來改善目前的交通狀況，而ITS (Intelligent Transportation System)就是在這個前提下提出的。
在ITS的系統中共分為七部分，其中的一項是APTS (Advance Public Transportation System)。在這個項目中，我們主要的目的是要利用科技來改善目前的公車營運狀況及行車安全。在此，智慧型站台是最受歡迎的。所以我們想利用影像處理、視訊…等技術來研發出一套即時的公車狀監視系統，因此我們想藉由影像處理、視訊方面的技術來達成此一目標。
在公車即時監視系統中，每部公車都該有自己獨一無二的身分。如此一來我們才能直接的判斷出車子的身分，並藉由圖形識別的技術將影像中的特徵擷取出來。所謂的"特徵"即是一個我們設計的特殊圖形，它代表了coming bus's identification。為什麼不用車牌直接辨識，是由於車牌辨識有其不正確性，目前現階段的研究對於數字辨識並沒有百分之百正確的把握。因此，我們想到或許可以用條碼來代替車牌進行辨識。由於，條碼所可代表的資料相當豐富且其隱密性高，再加上又有自我修正的能力，故而很適合應用在此。但由於一般市面的條碼必須近距離才能正確讀取，因此我們特別將條碼放大到可以讀取的範圍，在藉由數位攝影機去讀取影像進而將條碼識別出來。
因此，在此系統中我們需要一部數位攝影機、一部個人電腦以及一部雷射印表機來達成我們預定完成的目標。

In the coming 21 century, the improvement of techniques is further than our thinking. Although they bring a lot of endless future and convenience for us, many problems come with them soon. Like transportation, a lot of vehicles retrench much time, but pollution, accident, reducing energy and crowed traffic also come with them. So we should use our modern technique well to improve the current traffic. ITS (Intelligent Transportation System) is proposed based on this idea.
ITS is consisted of seven parts; one of them is APTS (Advanced Public Transportation System). It plays an important role to overcome the traffic problem. If we can know where is the bus at any time, it would be the aid of traffic management or passengers take a bus. Here we choose the technique of pattern recognition and image processing to apply this system and we present an efficient approach to solve this issue in this thesis.
It is convenient for passengers to take a bus if they know the sequence of the coming bus. This is the reason why we choose the barcode recognition technique. It is hard for the wireless technique to procure this purpose.
We use the property of the RGB model to separate the barcode from the original image and then find the edge according to the gradient. Finally we capture the barcode by its width to height rate. We decode the barcode after capturing it. In this step, we try to find the binary thresholding value first. We decode the barcode according to the width of each element at last.
In the experiment, we use a digital camera to verify that our method is practicable. We miss once in the 33 rounds. We have 383 valid images, 356 successful images and 27 rejecting image in these thirty-three rounds.

ABSTRACT
ACKNOWLEDGMENTS
TABLE OF CONTENTS
LIST OF TABLES
LIST OF FIGURES
CHAPTER 1 INTRODUCTION
1.1 Motivation
1.2 Proposed Method
1.3 Organization of thesis
CHAPTER 2 Related Work
2.1 A brief review of Bar Code
2.1.1 1D barcode
2.1.2 2D barcode
2.2 Survey on locating pattern
2.2.1 Locate the pattern in color based
2.2.2 Locate the pattern with filter
2.3.3 Other reference about this issue
CHAPTER 3 Code Construction and Error Correction
3.1 Code39 Construction
3.2 Error Correction
CHAPTER 4 PATTERN GENERATOR AND READER
4.1 Pattern generator
4.1.1 Pattern Encoding
4.1.2 Features addition
4.1.3 Pattern printing
4.2 Pattern reader
4.2.1 Pattern Locating
4.2.1.1 Color Filter
4.2.1.2 Continuous Points
4.2.2 Pattern Decoding
4.2.2.1 Binary Thresholding
4.2.2.2 Scan the Image
4.2.2.3 Decode the Number
CHAPTER 5 EXPERIMENTAL RESULTS
5.1 The mode of the image gathering
5.2 Experiment Analysis
5.2.1 System Architecture in experiment
5.2.2 The environment of the experiment
5.2.3 Experimental Result
5.3 Discussing of Experiment
CHAPTER 6 CONCLUSION AND FUTURE WORKS
Reference

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