臺灣博碩士論文加值系統

本論文提出一個利用投影技巧進行交通標誌偵測並利用投影、矩量及馬可夫模型進行辨識的新方法。首先從照片中偵測出交通號誌所在之位置，再從偵測位置擷取出交通標誌的影像特徵，與預先建立的交通標誌資料庫作比對，以達到辨識交通標誌的目的。
進而言之，首先將RGB色彩系統表示之彩色影像轉換到HSV色彩系統，進行色彩量化，再利用交通標誌具有特殊顏色的特性，進行整張影像水平垂直投影，以偵測出交通標誌所在位置。在辨識階段，則在偵測出可能號誌位置進行局部區域之特徵擷取。再採用兩階段式策略進行辨識工作。首先利用局部區域之背景水平及垂直的投影比對，篩選出資料庫中可能的交通號誌候選者。再利用局部區域之前景水平及垂直的投影配合矩量進行比對，或者利用馬可夫模型比對，將候選者加以排序，最後結合兩種比對方式的結果，做出最後排名，並以排名第一者為辨識結果。由實驗結果證實所提方法確實有效。

This study proposes a novel road sign detection and recognition method. The position of road sign in an image is detected using the projection techniques. The features of road sign are then extracted using the techniques of projection, moment and Markov model, which, in turn are used to match the detected road sign to those in the database so that the goal of road sign recognition can be achieved.
More specifically, the color images in terms of RGB color system are first converted to HSV color system and then quantized into specific colors existing in road signs. The horizontal and vertical projections of whole images in the specific colors are then used to detect the positions of road signs. In the recognition stage, only local features around the detected positions are used and two-step strategy is adopted. The horizontal and vertical projections of background in local area are used to prune irrelevant road signs. The candidate road signs are then sorted by the horizontal and vertical projections of foreground together with moment or by the techniques of Markov model. The two ranking results are integrated into the final consensus and the one with the first rank is regarded as the recognition result. The effectiveness of the proposed method has been demonstrated by various experiments.

摘要 i
Abstract ii
誌謝 iii
Contents iv
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
1.1. Motivation 1
1.2. Survey of Related Studies 1
1.3. Proposed Approach 3
1.4. Organization of the Thesis 3
Chapter 2 Feature Extraction 4
2.1. Color Quantization 4
2.2. Projection Profile 6
2.3. Moment 7
2.4. The Markov Model 9
2.5. Template Matching 13
Chapter 3 Road Sign Detection 14
3.1. Detection of Position of Road Sign 14
3.2. Extraction of Region of Interest 17
Chapter 4 Road Sign Recognition 21
4.1. Pruning Function Design 21
4.2. Matching Function Design 22
Chapter 5 Road Sign Database 27
Chapter 6 Experimental Results 29
6.1. Recognition Accuracy Estimation 30
6.2. Recognition Average Rank Estimation 30
6.3. Error Analysis 31
Chapter 7 Conclusion and Future Work 34
References 35

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