C语言图像处理实战指南

需积分: 9 115 浏览量更新于2024-07-31 收藏 9.21MB PDF 举报

"Image Processing in C - Second Edition" by Dwayne Phillips 本书是关于C语言图像处理的教程，旨在引导读者理解图像处理的基本概念并提供实际操作的代码示例。作者Dwayne Phillips在1994年首次出版了这本书，后来经过电子版更新，版权归属至2000年的Dwayne Phillips。读者如果购买了电子版，有权打印一份副本。在本书中，每一章都会通过文字、图表和照片来解释图像处理的概念，并用C语言实现这些操作。这些内容来源于作者在"The C/C++ Users Journal"上发表的文章。该期刊现在由CMP Media, Inc.出版，推荐读者关注此期刊以获取更多相关知识。书中提供的源代码可以在以下链接找到：http://members.aol.com/dwaynephil/cips2edsrc.zip。这使得读者能够直接下载和研究与书中的示例对应的C语言程序，从而更深入地理解和应用图像处理技术。图像处理是一个广泛的领域，包括图像的获取、分析、增强、压缩和复原等。在C语言中实现图像处理涉及到像素操作、颜色空间转换、滤波、边缘检测、形态学操作等技术。例如，C语言可以用来进行灰度转换、直方图均衡化、卷积滤波、二值化等基本图像处理任务。在学习C语言图像处理时，读者需要了解基本的数据结构，如二维数组，用于存储图像的像素信息；还需要掌握基本的文件操作，因为图像通常以文件形式存储和读取。此外，对于更复杂的算法，如傅立叶变换或小波分析，可能需要用到数值计算库，如FFTW或OpenCV。通过这本书，读者不仅可以学习到图像处理的基本理论，还可以获得实际编程的经验，这对于计算机视觉、数字信号处理、机器学习等相关领域的深入学习都是非常有益的。同时，C语言的高效性和灵活性使得它成为进行高性能计算和嵌入式系统开发的理想选择。 "Image Processing in C"是一本适合初学者和有一定编程基础的读者的教程，它将理论与实践相结合，帮助读者掌握图像处理的核心技术和C语言编程技巧。通过这本书的学习，读者可以具备开发自己的图像处理应用程序的能力，从而在科研或工程实践中发挥重要作用。

xiv LIST OF FIGURES

5.7 The Result of the Sobel Masks . . . . . . . . . . . . . . . . . . 54

5.8 The Result of the Sobel Masks Without Thresholding . . . . . 55

5.9 The Result of the Quick Mask . . . . . . . . . . . . . . . . . . 55

6.1 Original House Image . . . . . . . . . . . . . . . . . . . . . . . 57

6.2 An Example of the Homogeneity Operator . . . . . . . . . . . 59

6.3 Result of Homogeneity Edge Detector . . . . . . . . . . . . . . 60

6.4 An Example of the Diﬀerence Operator . . . . . . . . . . . . . 61

6.5 Result of Diﬀerence Edge Detector . . . . . . . . . . . . . . . 62

6.6 Gaussian “Mexican Hat” Masks . . . . . . . . . . . . . . . . . 63

6.7 Detecting Small Edges . . . . . . . . . . . . . . . . . . . . . . 64

6.8 Result of Gaussian Edge Detector with 7x7 Mask . . . . . . . 65

6.9 Result of Gaussian Edge Detector with 9x9 Mask . . . . . . . 66

6.10 Contrast-Based Edge Detector . . . . . . . . . . . . . . . . . . 67

6.11 Result of Quick Edge Detector . . . . . . . . . . . . . . . . . . 68

6.12 Result of Contrast-Based Edge Detector . . . . . . . . . . . . 69

6.13 Result of Edge Enhancement . . . . . . . . . . . . . . . . . . . 70

6.14 The Results of Applying the Variance and Range Operators

to an Array of Numbers . . . . . . . . . . . . . . . . . . . . . 71

6.15 Result of Variance Edge Detector . . . . . . . . . . . . . . . . 72

6.16 Result of Range Edge Detector . . . . . . . . . . . . . . . . . 72

7.1 Side View of an Image with Low Spatial Frequencies . . . . . 76

7.2 Side View of an Image with High Spatial Frequencies . . . . . 76

7.3 Low-Pass Filter Convolution Masks . . . . . . . . . . . . . . . 78

7.4 An Image Segment with Low Spatial Frequencies . . . . . . . 79

7.5 An Image Segment with High Spatial Frequencies . . . . . . . 79

7.6 Low-Pass Filtering of Figure 7.4 . . . . . . . . . . . . . . . . . 80

7.7 Low-Pass Filtering of Figure 7.5 . . . . . . . . . . . . . . . . . 81

7.8 Noisy Aerial Image . . . . . . . . . . . . . . . . . . . . . . . . 82

7.9 Result of Low-Pass Filter Mask #6 . . . . . . . . . . . . . . . 82

7.10 Result of Low-Pass Filter Mask #9 . . . . . . . . . . . . . . . 83

7.11 Result of Low-Pass Filter Mask #10 . . . . . . . . . . . . . . 83

7.12 Result of Low-Pass Filter Mask #16 . . . . . . . . . . . . . . 84

7.13 Result of 3x3 Median Filter . . . . . . . . . . . . . . . . . . . 84

7.14 House Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

7.15 Result of 3x3 Median Filter . . . . . . . . . . . . . . . . . . . 86

7.16 Result of 5x5 Median Filter . . . . . . . . . . . . . . . . . . . 86

LIST OF FIGURES xv

7.17 Result of 7x7 Median Filter . . . . . . . . . . . . . . . . . . . 87

7.18 High-Pass Filter Convolution Masks . . . . . . . . . . . . . . . 89

7.19 Result of High-Pass Filter on Figure 7.4 . . . . . . . . . . . . 89

7.20 Result of High-Pass Filter on Figure 7.5 . . . . . . . . . . . . 90

7.21 Result of High-Pass Filter Mask #1 . . . . . . . . . . . . . . . 91

7.22 Result of High-Pass Filter Mask #2 . . . . . . . . . . . . . . . 91

7.23 Result of High-Pass Filter Mask #3 . . . . . . . . . . . . . . . 92

8.1 Addition and Subtraction of Images . . . . . . . . . . . . . . . 96

8.2 A House Image . . . . . . . . . . . . . . . . . . . . . . . . . . 96

8.3 Edge Detector Output of Figure 8.2 . . . . . . . . . . . . . . . 97

8.4 Figure 8.2 Minus Figure 8.3 (Edges Subtracted) . . . . . . . . 97

8.5 Cutting and Pasting . . . . . . . . . . . . . . . . . . . . . . . 98

8.6 Section of Figure 8.3 Cut and Pasted Into Figure 8.2 . . . . . 99

8.7 Two Images Pasted Onto a Blank Image . . . . . . . . . . . . 100

9.1 An Image Example . . . . . . . . . . . . . . . . . . . . . . . . 104

9.2 A Histogram of the Image of Figure 9.1 . . . . . . . . . . . . . 105

9.3 The Image in Figure 9.1 with All the Pixels Except the 8s

Blanked Out . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

9.4 Figure 9.1 with a Threshold Point of 5 . . . . . . . . . . . . . 106

9.5 Aerial Image with Poor Contrast . . . . . . . . . . . . . . . . 107

9.6 Result of Histogram Equalization on Figure 9.5 . . . . . . . . 108

9.7 Result of High-Pass Filtering on Figure 9.6 . . . . . . . . . . . 109

9.8 The Result of Smoothing the Histogram Given in Figure 9.2 . 110

9.9 The Result of Correctly Thresholding Figure 9.1 . . . . . . . . 111

9.10 The Result of Region Growing Performed on Figure 9.9 . . . . 112

9.11 Pseudocode for Region Growing . . . . . . . . . . . . . . . . . 114

9.12 Input Image for Segmentation Examples . . . . . . . . . . . . 115

9.13 Threshold of Figure 9.12 with High=255 and Low=125 . . . . 115

9.14 Threshold of Figure 9.12 with High=255 and Low=175 . . . . 116

9.15 Threshold of Figure 9.12 with High=255 and Low=225 . . . . 116

9.16 Result of Incorrect Peak Separation . . . . . . . . . . . . . . . 118

9.17 A Histogram in which the Highest Peak Does Not Correspond

to the Background . . . . . . . . . . . . . . . . . . . . . . . . 119

9.18 Threshold of Figure 9.12 Using Peak Technique (High=255

and Low=166) . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

xvi LIST OF FIGURES

9.19 Threshold of Figure 9.12 Using Valley Technique (High=255

and Low=241) . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

9.20 Threshold of Figure 9.12 Using Adaptive Technique (High=255

and Low=149) . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

10.1 Using Edges to Segment an Image . . . . . . . . . . . . . . . . 126

10.2 Growing Objects Using Gray Shades . . . . . . . . . . . . . . 127

10.3 Growing Objects Using Gray Shades and Edges . . . . . . . . 128

10.4 Aerial Image of House Trailers . . . . . . . . . . . . . . . . . . 129

10.5 House Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

10.6 Edge Detector Output from Figure 10.5 . . . . . . . . . . . . . 130

10.7 A Small Edge Detector Error Leads to a Big Segmentation Error131

10.8 Edge Detector Output from Figure 10.4 . . . . . . . . . . . . . 132

10.9 Triple-Thick Edges Distort Objects . . . . . . . . . . . . . . . 133

10.10Result of Mistaking Edges for Objects . . . . . . . . . . . . . 134

10.11Output of Median, High-Pixel, and Low-Pixel Filters . . . . . 135

10.12Low-Pixel Filtering Performed on Figure 10.5 . . . . . . . . . 136

10.13Edge Detector Output from Figure 10.12 . . . . . . . . . . . . 137

10.14Edge Detector Output from Figure 10.4 — Thresholded at 70%138

10.15Result of Eroding Stray Edges . . . . . . . . . . . . . . . . . . 139

10.16Eroding Away Thick Edges . . . . . . . . . . . . . . . . . . . 140

10.17Result of Eroding the Edges in Figure 10.13 . . . . . . . . . . 141

10.18The Region Growing Algorithm from Chapter 9 . . . . . . . . 142

10.19The Improved Region Growing Algorithm (Part 1) . . . . . . 143

10.19The Improved Region Growing Algorithm (Part 2) . . . . . . 144

10.20Sobel Edge Detector Output from Figure 10.4 (after Erosion) . 147

10.21Result of Edge-Only Segmentation of Figure 10.4 . . . . . . . 148

10.22Result of Gray-Shade-Only Segmentation of Figure 10.4 . . . . 148

10.23Result of Edge and Gray Shade Segmentation of Figure 10.4 . 149

10.24Result of Edge-Only Segmentation of Figure 10.5 . . . . . . . 150

10.25Result of Gray-Shade-Only Segmentation of Figure 10.5 . . . . 150

10.26Result of Edge and Gray Shade Segmentation of Figure 10.5 . 151

11.1 Aerial Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

11.2 Segmentation of Aerial Image . . . . . . . . . . . . . . . . . . 154

11.3 House Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

11.4 Segmentation of House Image . . . . . . . . . . . . . . . . . . 155

11.5 A Binary Image . . . . . . . . . . . . . . . . . . . . . . . . . . 156

LIST OF FIGURES xvii

11.6 The Result of Eroding Figure 11.5 . . . . . . . . . . . . . . . . 156

11.7 The Result of Dilating Figure 11.5 . . . . . . . . . . . . . . . 157

11.8 The Result of Eroding Figure 11.5 Using a Threshold of 2 . . 158

11.9 The Result of Dilating Figure 11.5 Using a Threshold of 2 . . 158

11.10Four 3x3 Masks . . . . . . . . . . . . . . . . . . . . . . . . . . 158

11.11The Result of Dilating Figure 11.5 with the Four Masks of

Figure 11.9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

11.12Examples of Masked Vertical and Horizontal Dilations . . . . 161

11.13Two Objects Joined by a Thread, Separated by opening and

a Hole Enlarged by opening . . . . . . . . . . . . . . . . . . . 162

11.14A Segmentation and the Result of Opening . . . . . . . . . . . 163

11.15Two Objects that Should be Joined, How closing Removes the

Break and Fills Unwanted Holes . . . . . . . . . . . . . . . . . 164

11.16An Unwanted Merging of Two Objects . . . . . . . . . . . . . 165

11.17Closing of Segmentation in Figure 11.2 . . . . . . . . . . . . . 166

11.18An Unwanted Splitting of an Object . . . . . . . . . . . . . . 167

11.19Result of Special Routines that Open and Close Objects but

do not Join or Break Them . . . . . . . . . . . . . . . . . . . 168

11.20Result of Opening of a 2-Wide Object . . . . . . . . . . . . . 169

11.21Cases Where Objects Can and Cannot be Eroded . . . . . . . 169

11.22Cases that do and do not Require a Special Closing Routine . 170

11.23Special Closing of Segmentation of Figure 11.2 . . . . . . . . . 171

11.24Erosion of Segmentation in Figure 11.4 . . . . . . . . . . . . . 172

11.25Special Closing of Figure 11.24 . . . . . . . . . . . . . . . . . 172

11.26Outline of Segmentation in Figure 11.4 . . . . . . . . . . . . . 173

11.27The Interior Outline of an Object . . . . . . . . . . . . . . . . 174

11.28The Exterior Outline of an Object . . . . . . . . . . . . . . . . 175

11.29Thinning a Rectangle until it is One Pixel Wide . . . . . . . . 176

11.30A Square, its Euclidean Distance Measure, and its Medial Axis

Transform (Part 1) . . . . . . . . . . . . . . . . . . . . . . . . 178

11.30A Square, its Euclidean Distance Measure, and its Medial Axis

Transform (Part 2) . . . . . . . . . . . . . . . . . . . . . . . . 179

11.31A Rectangle and its Medial Axis Transform . . . . . . . . . . 180

11.32(Clockwise from Upper Left) A, Its Outline, Medial Axis Trans-

form, and Thinning . . . . . . . . . . . . . . . . . . . . . . . . 181

12.1 Existing Standard TIFF Tags . . . . . . . . . . . . . . . . . . 184

12.2 Original Aerial Image . . . . . . . . . . . . . . . . . . . . . . . 185

xviii LIST OF FIGURES

12.3 Segmentation of Aerial Image (from Chapter 10) . . . . . . . . 185

12.4 Segmented Aerial Image Masked with Original . . . . . . . . . 186

12.5 ilabel Output on Left, Dilation in Center, XOR of Both on Right187

12.6 Labeled Boy Image . . . . . . . . . . . . . . . . . . . . . . . . 187

12.7 Images A and B . . . . . . . . . . . . . . . . . . . . . . . . . . 189

12.8 Result of Overlay Non-Zero A . . . . . . . . . . . . . . . . . . 190

12.9 Result of Overlay Zero A . . . . . . . . . . . . . . . . . . . . . 190

12.10Result of Overlay Greater A . . . . . . . . . . . . . . . . . . . 191

12.11Result of Overlay Less A . . . . . . . . . . . . . . . . . . . . . 191

12.12Result of Average Overlay . . . . . . . . . . . . . . . . . . . . 192

12.13Two Images Side by Side . . . . . . . . . . . . . . . . . . . . . 192

12.14Two Images Averaged . . . . . . . . . . . . . . . . . . . . . . 193

12.15Leafy Texture Image . . . . . . . . . . . . . . . . . . . . . . . 193

12.16House Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

12.17Averaging Leafy Texture and House Image . . . . . . . . . . . 194

12.18White Frame in Blank Image . . . . . . . . . . . . . . . . . . 195

12.19Frame Overlaid on Boy Image . . . . . . . . . . . . . . . . . . 196

13.1 The Three Basic Geometric Operations: Displacement, Stretch-

ing, and Rotation . . . . . . . . . . . . . . . . . . . . . . . . . 198

13.2 Examples of Displacement . . . . . . . . . . . . . . . . . . . . 199

13.3 Examples of Stretching . . . . . . . . . . . . . . . . . . . . . . 200

13.4 Examples of Rotation about the Origin . . . . . . . . . . . . . 200

13.5 Examples of Cross Products . . . . . . . . . . . . . . . . . . . 201

13.6 Combining All Four Geometric Operations . . . . . . . . . . . 202

13.7 Rotation About any Point m,n . . . . . . . . . . . . . . . . . . 203

13.8 Examples of Rotation About Any Point . . . . . . . . . . . . . 204

13.9 A Comparison of Not Using Bi-Linear Interpolation and Using

Bi-Linear Interpolation . . . . . . . . . . . . . . . . . . . . . . 205

13.10Bi-Linear Interpolation . . . . . . . . . . . . . . . . . . . . . . 206

13.11The Boy Image Enlarged Horizontally and Shrunk Vertically . 207

14.1 Bi-Linear Interpolation of a Quadrilateral . . . . . . . . . . . 211

14.2 The Control Point Warping Process . . . . . . . . . . . . . . . 213

14.3 Examples of Control Point Warping . . . . . . . . . . . . . . . 215

14.4 The Object Warping Process . . . . . . . . . . . . . . . . . . . 215

14.5 Examples of Object Warping . . . . . . . . . . . . . . . . . . . 217

14.6 Warped House Image . . . . . . . . . . . . . . . . . . . . . . . 217

剩余815页未读，继续阅读

yincheol

粉丝: 0
资源: 7

C语言图像处理实战指南

Image processing in c

A Simplified approach to image processing in C: Classical and Modern Techniques in C

matlab图像处理英文文献,matlab图像处理中英文翻译文献

用Python实现文献[6] J.-H. Syu, S.-J. Wang, and L.-C. Wang, “Hierarchical image segmentation based on iterative contraction and merging,” IEEE Transactions on Image Processing, vol. 26, no. 5, pp. 2246–2260, May 2017.d

用Python实现文献J.-H. Syu, S.-J. Wang, and L.-C. Wang, “Hierarchical image segmentation based on iterative contraction and merging,” IEEE Transactions on Image Processing, vol. 26, no. 5, pp. 2246–2260, May 2017.的迭代收缩与合并算法

推荐几本学习椭圆滤波器的书籍，该书籍有相关代码

基于传统方法的三维缺陷检测有哪些文献

C=imextendedmax(A,2)

请帮我用matlab复现2016-2017年的DBF算法的相关论文

关于机器视觉的外语参考文献

最新资源