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研究生:曾筱芸
研究生(外文):Hsiao-Yun Tseng
論文名稱:影像切割之研究及其應用
論文名稱(外文):A Study on Image Segmentations and Applications
指導教授:喻石生喻石生引用關係
指導教授(外文):Shyr-Shen Yu
學位類別:碩士
校院名稱:國立中興大學
系所名稱:資訊科學與工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:47
中文關鍵詞:車牌辨識邊緣偵測形態學原生寄生蟲細胞切割法gamma均化two-means 分群法
外文關鍵詞:License plate recognition (LPR)edge detectionmorphologyprotozoan parasitescell segmentation schemegamma equalizationtwo-means clustering
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近三十年來,數位影像處理技術被廣泛的應用在幾何變換、影像壓縮、影像增強、影像切割等方面。所謂的影像切割是將感興趣的部份從背景取出的一種技術。然而,切割影像在電腦視覺上是一個相當大的挑戰,因為它將深深影響往後影像識別的正確率。本篇論文運用一些影像處理技術於處理車牌偵測及細胞切割。
車牌偵測在車牌辨識系統中扮演著相當重要的角色。如果車牌能夠被準確的偵測,對於接下來的字元切割及字元辨識會有事半功倍的效果。在我們的觀察中,影像的照度(illumination)將會影響車牌偵測。因此本論文的目標之一是在複雜背景且拍攝於不同情境的影像中擷取一張以上的車牌。車牌偵測的方法包含六個步驟:(1) 將彩色影像轉換為灰階影像 (2)影像均化 (3)邊緣偵測 (4)檢查黑色象素比例(5)車牌確認(6)輸出車牌。實驗結果顯示,本論文提出的方法在GetRatio達到94%的正確率; 在GetRight擁有83%的正確率。
致病性的原生寄生蟲會使人類得到許多疾病,例如:霍亂、傷寒及阿米巴症等等。目前,數位影像被廣泛使用在醫學領域幫助醫生及病理學家分析病理學上的問題及診斷疾病。本論文提出一個具有準確切割能力的數位影像處理技術將原生寄生蟲從一張影像品質較差或擁有複雜背景的顯微影像中切割出來。在實驗結果中,此方法可以獲得96.64%的正確率。除此之外,在ME、RN、EFAE三個錯誤率計算當中,其值分別只有0.04、0.45及0.06。
Digital image processing is a widely applied technology in recent thirty years. Geometric transformations, image compression, image enhancement, image segmentation are some of the most used digital image processing techniques. Image segmentation is employed to distinguish the region of interest (ROI) from background. However, segmenting image is a great challenge for computer vision because the accuracy of image segmentation highly affects the accuracy of followed image recognition. In this thesis, some image processing techniques are proposed to deal with license plate detection and cell segmentation.
License plate detection plays an important role in license plate recognition (LPR) system. If license plates can be located exactly, the character segmentation and recognition can be implemented more precisely and efficiently. In our observation, the illumination affects the result of locating a license plate. One aim of this thesis is to extract more than one license plate from an image and to obtain the license plates in the image taken under different conditions. The proposed method includes six processes as follows: transferring color to grayscale, image equalization, edge detection, checking black pixel ratio, plate verification and output license plates. The experimental results show that the proposed method can gain 94% accuracy in GetRatio and 83% accuracy in GetRight.
The pathogenic protozoan parasites can cause human to get many diseases, such as, cholera, typhoid fever, and amoebiasis, etc. Digital images are extensively applied to medical fields for doctors and pathologists to analyze pathological sections and further diagnose the disease. A digital image processing scheme is prosposed to segment protozoan parasites from protozoan parasite microscopic images. The proposed image processing scheme has precise segmentation ability even if the image is with poor quality or complex background. The experimental results show that the proposed scheme can gain 96.64% correct rate (TN+TP), and about 0.04, 0.45 and 0.06 of the average error rates: ME, RN and EFAE, respectively.
Contents............................................ i
List of Figures..................................... iii
List of Tables...................................... v
Chapter 1 Introduction.............................. 1
1.1 Preface......................................... 1
1.2 Image quantization.............................. 3
1.3 Digital images .................................. 4
1.4 Image filtration................................ 5
1.5 Image segmentation.............................. 7
1.5.1 Multilevel thresholding.................... 8
1.5.2 Region-based segmentation.................. 9
1.5.3 Segmentation by Watersheds................. 10
1.5.4 Classification............................. 10
Chapter 2 Related Works............................. 12
2.1 Color image processing.......................... 12
2.2 Image enhancement............................... 13
2.2.1 Histogram equalization..................... 13
2.2.2 Local area enhancement..................... 14
2.3 Image binarization.............................. 15
2.3.1 Otsu’s method............................. 15
2.3.2 Yasuo et al’s method...................... 16
2.4 Edge detection.................................. 17
2.4.1 Sobel edge detection....................... 17
2.4.2 Canny edge detection....................... 18
2.5 Mathematical morphology......................... 18
2.6 License plate extraction........................ 19
2.7 Cell segmentation............................... 20
Chapter 3 An Effective License-Plate Detection Method for Overexposure and Complex Vehicle Images........... 22
3.1 Proposed method................................. 22
3.1.1 Transferring color to grayscale............ 22
3.1.2 Image equalization ......................... 24
3.1.3 Edge detection............................. 25
3.1.4 Checking black pixel ratio................. 26
3.1.5 Plate verification ........................ 28
3.2 Experimental results........................... 29
Chapter 4 A Cell Segmentation Scheme for Digital Protozoan Parasite Microscopic Images........................ 32
4.1 Proposed method................................ 32
4.1.1 Color space transforming.................. 32
4.1.2 Gamma equalization ........................ 33
4.1.3 Median-mean filter ........................ 35
4.1.4 Two-classes edge enhancement.............. 36
4.1.5 Two-means clustering method............... 36
4.1.6 Largest independent component detection... 38
4.2 Experiment results............................. 40
Chapter 5 Conclusions.............................. 43
Bibliography....................................... 45

List of Figures

Fig.1.4-1 Mean filter mask ........................ 6
Fig.1.4-2 An example for median filter............ 7
Fig.1.5-1 A simple example of image thrsholding method 9
Fig.1.5-2 Connected component criteria............ 9
Fig.1.5-3 A basic neural network model............. 11
Fig.2.2-1 The gray-scale transformation function... 14
Fig.2.4-1 Sobel masks.............................. 17
Fig.2.6-1 The process of feature extraction stage... 20
Fig.3.1-1 The overview of the proposed method....... 23
Fig.3.1-2 The gray-level histogram of a brighter/ darker image and the gray-level histogram after performing equalization......................................... 24
Fig.3.1-3 Traditional histogram equalization/ equalization mapping with linear interpolation.................... 25
Fig.3.1-4 An example for revising the vertical size of a
license plate........................................ 27
Fig.3.1-5 An example for revising the horizontal size of a
license plate........................................ 27
Fig.3.1-6 Revise license plate...................... 27
Fig.3.1-7 The results of morphological process...... 28
Fig.3.1-8 A 4-neighbor structuring element.......... 28
Fig.3.1-9 Histogram of a binary license plate candidate
..................................................... 29
Fig.3.2-1 Original color image...................... 30
Fig.3.2-2 Grayscale image........................... 30
Fig.3.2-3 Equalization image........................ 30
Fig.3.2-4 Edge detection image (T1 = 200)........... 30
Fig.3.2-5 The results after checking black pixel ratio (T2
= 500)/ histogram analysis of plate erification/ results of plate verification.................................... 31
Fig.4.1-1 A digital protozoan parasite image of Entamoeba
histolytica/ The result of applying gray-level transforming to the protozoan...................................... 33
Fig.4.1-2 A description of the low-bound and the up-bound
in gamma equalization................................. 34
Fig.4.1-3 The results of gamma equalization using different parameters (α and γ)....................... 35
Fig.4.1-4 The result image of applying median-mean filter
..................................................... 35
Fig.4.1-5 The result of applying two-classes edge
enhancement........................................... 36
Fig.4.1-6 The result of applying two-means clustering
method................................................. 37
Fig.4.1-7 The scanning directions and referred neighbors
of different scanning models.......................... 38
Fig.4.1-8 The largest independent components obtained from
Fig. 4.1-6 in different index scanning models......... 39
Fig.4.1-9 The result obtained by combining Fig. 4.1-8 (a)-
(d) with OR operation/ The result of applying inter-size filling method to former.............................. 40
Fig.4.1-10 The comparison between original and processed
image................................................. 40
Fig.4.2-1 The original image/ the results obtained without
equalization/ the results of using gamma equalization. 41

List of Tables

Table.3.2-1 GetRatio in each image................... 31
Table.3.2-2 GetRight in each image................... 31
Table.3.2-3 Compare with Chen et al.’s method....... 31
Table.4.1-1 The means and ranges of TP, TN, FP and FN for
the proposed scheme without equalization or with gamma equalization.......................................... 42
Table.4.1-2 The means and ranges of ME, RN and EFAE for the
proposed scheme without equalization or with gamma equalization......................................... 42
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