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研究生：	林宜賢 Lin, Yi-hsien
論文名稱：	攝影機自動對焦技術與強韌模式辨認之研究 A Study on Camera Autofocusing and Robust Template Matching
指導教授：	陳進興 Chen, Chin-Hsing
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2007
畢業學年度：	95
語文別：	英文
論文頁數：	111
中文關鍵詞：	次像素、參數樣版向量、環形投影轉換、自動對焦、尖銳函數
外文關鍵詞：	Subpixel, Ring projection transform, Sharpness function, Parametric template vector, Autofocus
相關次數：	點閱：121 下載：3
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本論文提出三種不同的方法分別使用在三種不同的應用上。在第一種方法裡，我們提出一種以小波轉換為基礎的攝影機自動對焦新技術，。因為小波模組可以保留邊界的資訊及對雜訊有極強的免疫力等優點。我們的方法裡的尖銳函數(sharpness function)的優點是其計算值分佈呈現出嚴謹單調(monotonic)，因此不會有區域性最大值(local maximum)的發生。實驗結果證實我們的方法可以讓攝影機成功的自動對焦。再者，方法觀念容易瞭解，容易完成且有效率。故適用於實際的自動對焦系統上。
在第二種方法裡，我們提出可以克服影像平移、旋轉及尺度大小有變化的模式辨識新方法。本方法首先使用環形投影轉換(ring projection transform)，將二維影像樣版(template)轉換成一維的信號。環形投影轉換的優點是擁有可以克服影像旋轉和降低計算複雜度等特性。接著將已轉換的不同大小尺度的一維樣版信號建構成一個一維參數樣版向量(parametric template vector)，再利用此參數樣版向量來做模式辨認的計算。本方法在模式辨認上不僅可以解決影像旋轉的問題，也可以克服影像尺度的變化。除此之外，本方法觀念容易瞭解，容易完成，且只需一張樣版影像作訓練。實驗證實本方法的計算速度比參數樣版(parametric template)法快約15倍，辨識成效也比參數樣版法好。本方法除了可以解決影像平移、旋轉及尺度大小有變化的問題，還可以估算出待測影像尺度變化的大小。含高斯雜訊的實驗證實本方法的確具有強韌性。本方法可以用在線上待測物影像存在平移、旋轉及尺度大小變化的即時辨識系統上。
最後部分，我們結合參數樣版方法和環形投影轉換方法，提出一個在有旋轉影像辨識定位上可達到次像素(subpixel)精確度的模式辨識方法。本方法因沒有涉及疊代運算，故很有效率且容易完成。模擬實驗結果證實本方法在待測影像有旋轉的情況下仍擁有次像素的高精確度。在尋找兩個目標物的距離的實際景物實驗裡，結果也顯示當待測影像有旋轉及平移的情況下，也可達到次像素的高精確度。這表示本方法在高精密度且待測物會旋轉的線上檢測系統上，確實可行。

This thesis proposes three different approaches for three different applications. In the first part of our approaches, a novel autofocusing technique based on the wavelet transform is proposed. Because wavelet modulus can preserve edges more precisely and it is almost noise free. The merits of our approach are that the distribution of sharpness function is strictly monotonic and global maximum is well defined. Experimental results show that our approach can locate the best focus successfully. Furthermore, our approach is conceptually simple, easy for implementation and very efficient. It is suitable for practical systems.
For the second part of our approaches, a new method of the template matching, invariant to image translation, rotation and scaling, is proposed. In the first step of the approach, the ring projection transform (RPT) process is used to convert a 2-D template in a circular region into a 1-D gray-level signal as a function of radius. The advantages of the RPT process are that it owns the characteristic of rotation invariance and reduces the computational complexity of normalized correlation (NC). Then, the template matching is performed by constructing a parametric template vector (PTV) of the 1-D gray-level signal with differently scaled templates of the object. The merits of our approach are that it not only obtains rotation invariance, but also scale invariance. Additionally, our approach is conceptually simple, easy to implement and only one training image is needed for the training phase. Experimental results show that the computational time of the proposed approach is about fifteen times faster and the performance is better than the parametric template (PT) method in the image rotation or scaling. Moreover, our approach not only enjoys high accuracy under the changes of translation, rotation and scale, but also can estimate the scaling value of the target object in the input scene. Experiments with Gaussian noise demonstrate that the proposed algorithm is robust to detect the target object with the changes of translation, orientation and scale. This indicates that our approach is suitable for on-line template matching with scene translation, rotation and scaling.
In the last part of our approaches, a new subpixel template matching approach that combines the parametric template method and the ring projection transform process is proposed. It not only achieves subpixel accuracy in location, but also offers rotation invariance in the subpixel template matching. Furthermore, our approach is conceptually simple, easy to implement, and very efficient because no iterative steps are involved. The simulated results show that our approach enjoys very high precision in the presence of image rotations. Experiments with real-world scenes demonstrate that the proposed method can reach subpixel accuracy for finding the distance between two target objects in the presence of rotations and translations. This indicates that our approach is suitable for accurate on-line template matching with scene rotations and translations.

Table of Contents
中文摘要..........................................................................................................	I
Abstract.........................................................................................................	IV
Acknowledgement.......................................................................................	VII
Table of Contents........................................................................................	VIII
List of Tables................................................................................................	XI
List of Figures..............................................................................................	XII
Chapter 1	Introduction...................................................................................	1
	1.1	Motivation.....................................................................................	1
	1.2	Survey............................................................................................	3
		1.2.1	Autofocusing techniques of camera.........................................	4
		1.2.2	Pixel-based template matching.................................................	7
		1.2.3	Template matching with subpixel accuracy...........................	12
	1.3	An overview of this thesis...........................................................	15
		1.3.1	Camera autofocusing approach……......................................	15
		1.3.2		Template matching with translation-rotation-scale invariance…….......................................................................	16
		1.3.3		Subpixel image matching with rotation invariance…............	17
	1.4	Outline of this thesis....................................................................	18
Chapter 2	The techniques of image preprocess............................................	19
	2.1	Introduction.................................................................................	19
	2.2	Wavelet transform (WT) ............................................................	19
		2.2.1	Review of 1-D wavelet transform..........................................	20
		2.2.2	2-D wavelet decomposition....................................................	21
	2.3	Ring projection transformation (RPT) ........................................	23
		2.3.1	Theorem..................................................................................	23
		2.3.2	The measurement of similarity...............................................	25
Chapter 3	Autofocusing approach using wavelet decomposition................	32
	3.1	Introduction.................................................................................	32
	3.2	The principle of the image formation..........................................	33
	3.3	Sharpness function.......................................................................	34
	3.4	Experimental results....................................................................	35
		3.4.1	The performance of sharpness function based on WT……...	37
		3.4.2	The performance comparisons for different sharpness
 			functions…………………………………………………….	38
Chapter 4	Template matching with translation-rotation-scale invariance....	49
	4.1	Introduction.................................................................................	49
	4.2	The parametric template vector (PTV) method...........................	50
	4.3	The algorithm of template matching...........................................	53
	4.4	Results of experiment..................................................................	54
		4.4.1	Speed test................................................................................	55
		4.4.2	Accuracy comparisons for image translation and scaling......	56
		4.4.3	Accuracy comparisons for image translations and rotations..	58
		4.4.4	Template matching for image translation, rotation and scaling
			................................................................................................	58
		4.4.5	Performance on noisy images.................................................	62
Chapter 5	Subpixel image matching with rotation invariance ....................	69
	5.1	Introduction ................................................................................	69
	5.2	Principle of subpixel template matching.....................................	70
	5.3	The algorithm of subpixel template matching.............................	72
	5.4	Generation of simulated images by the downsampling algorithm
		.....................................................................................................	73
	5.5	Verification of the proposed algorithm using simulated images..	74
		5.5.1	Accuracy and computation-time comparisons for image Translations............................................................................	75
		5.5.2		Accuracy comparisons for image rotations and translations..	78
	5.6	Experimental verification............................................................	79
Chapter 6	Conclusions and future works.....................................................	89
References.....................................................................................................	93
Biography.....................................................................................................	109
 
List of Tables
Table 3.1	The comparison results of the average run time for the four sharpness functions…………………………………………….	39
Table 4.1	Comparison results on the total run time (sec) for the two
	methods studied……..………………………………………….	56
Table 4.2	Matching results of translated, rotated and scaled image for the
	test image A…………………………………………….………	60
Table 4.3	Matching results of translated, rotated and scaled image for the
	test image B…………………………………….………………	61
Table 5.1	The comparison results of the accuracy and computation time
	with image translations for the three methods studied…………	77
Table 5.2	The statistical results for the absolute error value between the distance of the two locations and the standard distance in the experiments with real world scenes............................................	81



 
List of Figures
Fig. 2.1	Wavelet analysis filter bank...........................................................	27
Fig. 2.2	A one-level wavelet decomposition of an   pixel image....	27
Fig. 2.3	A two-level WT using Haar wavelet..............................................	28
Fig. 2.4	A two-level WT using Daubechies wavelet…...............................	29
Fig. 2.5 	Concept of RPT for a template.......................................................	30
Fig. 2.6	Character ‘N’ image in two different orientations: 
	(a), (b) the original images; (c), (d) the corresponding plots of RPT.	……………………………………………………………………	30
Fig. 2.7 	Character ‘Y’ image in two different orientations: 	
	(a), (b) the original images; (c), (d) the corresponding plots of RPT……………………………………………………………....	31
Fig. 3.1	Image formation in a convex lens..................................................	40
Fig. 3.2	Block diagram of the automatic focusing system in my work…...	41
Fig. 3.3	The Text test image with size   used in the experiment......................................................................................	41
Fig. 3.4	Focus measure by HWTSF at resolution level 1, 2 and 3, respectively.....................................................................................	42
Fig. 3.5	Focus measure by DWTSF at resolution level 1, 2 and 3, Respectively...................................................................................	43
Fig. 3.6	Two different test images with size   used in the experiments....................................................................................	44
Fig. 3.7	Performance comparisons for the four methods studied in the Text test image.......................................................................................	45
Fig. 3.8	The results of optimal focus image for the four methods studied in the Text test image..........................................................................	46
Fig. 3.9	Performance comparisons for the four methods studied in the BGA test image.......................................................................................	47
Fig. 3.10	The results of optimal focus image for the four methods studied in the BGA test image........................................................................	48
Fig. 4.1	The diagram for construction of a PTV..........................................	63
Fig. 4.2	The PTV and its vertex template vector of the differently scaled templates........................................................................................	64
Fig. 4.3	Two different test images and the corresponding reference templates used in the experiment: (a) The test image A and its reference template with size 131×131,	(b) The test image B and its reference template with size 171×171...........................................................	65
Fig. 4.4 	Accuracy comparisons with image translation and scaling for the two methods studied in the test image A........................................	66
Fig. 4.5	Accuracy comparisons with image translation and scaling for the two methods studied in the test image B........................................	66
Fig. 4.6	Accuracy comparisons with image translations and rotations for 	the two methods studied in the test image A........................................	67
Fig. 4.7 	Accuracy comparisons with image translations and rotations for 	the two methods studied in the test image B........................................	67
Fig. 4.8 	Correct matching ratio with Gaussian noise under image 	translation, rotation and scaling........................................................................	68
Fig. 5.1	Diagram of a parametric template space in CPTRPT....................	82
Fig. 5.2	Three different test images and the corresponding reference templates used in the experiment: (a) IC Mark test image and reference template with size 91×91, (b) PLCC Lead test image and reference template with size 41×41, (c) Rigid Body test image and reference template with size 61×61................................................	83
Fig. 5.3	Accuracy comparisons with image rotations and translations
	for the three methods studied in the IC Mark test image...............	84
Fig. 5.4	Accuracy comparisons with image rotations and translations
 	for the three methods studied in the PLCC Lead test image..........	85
Fig. 5.5	Accuracy comparisons with image rotations and translations 	for the three methods studied in the Rigid Body test image......................	86
Fig. 5.6	A PC board image with two target objects obtained by CCD: (a) PC board image, (b) The images of the two target objects used as the two matching templates.................................................................	87
Fig. 5.7	Images showing the rotation range used in the experiments: (a) and (b) the two extreme images for rotation in the clockwise direction, (c) and (d) the two extreme images for rotation in the counterclockwise direction............................................................	88
                                    

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