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图像处理编程基础

图像处理编程基础. Media. 图形＆图像. 计算机图形学. 形. 像. 图像处理、计算机视觉. 有什么用？. 图像编辑（ photoshop ） 数字娱乐 生物医学 相机辅助拍摄 电子警察、安全检测 城市建模、导游 信息检索（基于内容的图像检索） 自动导航 …………. A picture is worth 1000 words … A video is worth 1000 sentences …. Rich information from visual data Examples of images around us

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图像处理编程基础

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  1. 图像处理编程基础

  2. Media

  3. 图形＆图像 计算机图形学 形 像 图像处理、计算机视觉

  4. 有什么用？ • 图像编辑（photoshop） • 数字娱乐 • 生物医学 • 相机辅助拍摄 • 电子警察、安全检测 • 城市建模、导游 • 信息检索（基于内容的图像检索） • 自动导航 • …………

  5. A picture is worth 1000 words…A video is worth 1000 sentences… • Rich information from visual data • Examples of images around us • Natural photographic images • Artistic and engineering drawings • Scientific images (satellite, medical, etc.) • Motion picture –video • Movies, TV programs, news • Family video • Surveillance and highway camera

  6. Why do we process images? • Enhancement and restoration • remove artifacts and scratches from an old photo/movie • improve contrast and correct blurred images • Transmission and storage • images from oversea via Internet, or from a remote planet • Information analysis and automated recognition • providing “human vision” to machines • Security and rights protection • encryption and watermarking

  7. Why Digital? • “Exactness” • Perfect reproduction without degradation • Perfect duplication of processing result • Convenient & powerful computer-aided processing • Can perform rather sophisticated processing through hardware or software • Even kindergartners can do it! • Easy storage and transmission • 1 CD can store hundreds of family photos! • Paperless transmission of high quality photos through network within seconds

  8. Human Vision System • Image is to be seen. • Perceptual Based Image Processing • Focus on perceptually significant information • Discard perceptually insignificant information • Issues: • Biological • Psychophysical

  9. Color • Color is the perceptual result of light having wavelength 400 nm to 700 nm that is incident upon the retina. • “Power distribution exists in the physical world, but color exists only in the eye and the brain.” • Does “red” mean the same to different people?

  10. Color Spectrum

  11. Grassman's First Law of Additive Color Mixture • Any color can be matched by a linear combination of three other colors (primaries, eg RGB), provided that none of those three can be matched by a combination of the other two. • C= Rc(R ) + Gc(G) + Bc(B)

  12. Color Spaces • RGB • CMY • CIE XYZ • s

  13. Different Image Types • Binary images (0 or 1) • Gray images (0~255) • Color images • indexed color images • full color images (24 bits per pixel, 8-red, 8-green, 8-blue) )

  14. A Binary Image

  15. Gray Images • 8 bits per pixel

  16. Full Color Images • 24 bits per pixel, and the three channels R G B are three gray images respectively

  17. 图像文件格式(1) • 矢量图（.ai, .eps, .ps, .pdf, .swf等） • draw circle • center 0.5, 0.5 • radius 0.4 • fill-color yellow • stroke-color black • stroke-width 0.05 • draw circle • center 0.35, 0.4 • radius 0.05 • fill-color black • …………

  18. 图像文件格式(2) • 位图（ .bmp, .jpg, .png, .gif等） a 光栅化 (rasterize)

  19. 图像（位图）的基本属性 • 分辨率（宽×高，像素、DPI） • 像素数（Megapixels） • 颜色空间（RGB, CMYK, YUV……） • 通道数（1，2，3，4，灰度＆彩色） • 位深度（8bits, 12bits,……LDR＆HDR） • 存贮坐标系： 左手坐标系 右手坐标系（如.bmp）

  20. 三原色：红，绿，蓝

  21. Color Components

  22. 图像表示 classCImage { intwidth, height; //分辨率 CPixel* data; //图像数据 }; class CPixel { UINT r, g, b; // red, green, blue values }; a

  23. 函数接口设计 BOOL Read(CFile* pFile); BOOL Write(CFile* pFile); int GetWidth(); int GetHeight(); RGBQUAD GetPixelAt(int x, int y); void SetPixelAt(int x, int y, BYTE r, BYTE g, BYTE b);

  24. 范例：CDib图像类

  25. 算法示例：几何变换与变形

  26. 图像坐标的映射

  27. 例子 F1 F2

  28. 变换函数 平移： 缩放： 旋转： x方向 切变 (shear)： y方向

  29. 齐次坐标与变换矩阵 • 使用变换矩阵的好处： • 使各种变换具有统一的形式； • 便于将多次变换合并； • 便于用数学工具进行分析； 齐次坐标： 2维仿射变换：

  30. Simple Transformations • Affine transformation • Perspective transformation

  31. = 平移（-cx, -cy） + 旋转 angle + 平移 （cx, cy） 示例：绕任意中心的旋转 void CalcRotateMatrix(Matrix23 &matrix, float angle, float cx, float cy);

  32. 正向变换＆逆向变换 正向： 源 目标 逆向： 目标 源

  33. 逆向变换：why？ 正向变换会造成目标图像中的部分像素没有被正确赋值！

  34. 最邻近插值 双线性插值 双三次插值 逆向变换：重采样

  35. 正向变换：网格 • 先将图像剖分成均匀网格，用正向变换将网格点变换到目标位置，再插值出中间像素的坐标

  36. 用网格下采样 • Point features • Partition

  37. 较复杂的变换

  38. 交互式图像变形

  39. Image Warping

  40. 用户交互：点对 • Point features

  41. Warping Problem • Scattered point interpolation • Given the values on some points, compute the value on arbitrary other point

  42. 散乱点插值问题 • Inverted distance method • Shepard, 1965 • Spline surface fitting • Thin-plate spline • Finite element method • Radial basis function • …

  43. 接口设计 CWarping { private: vector<Cline> m_pntSpecs; public: CPoint Warping(CPoint point); int Prepare(); };

  44. Inverted Distance Method • 用距离加权得到值

  45. Radial Basis Functions

  46. Radial Basis Functions

  47. Examples

  48. More Examples

  49. Project 1: Image Warping • 用CDib类实现 • 实现至少2种warping方法，包括Inverted weighted distance和Radial basis function方法（读参考文献） • 写详细测试报告 • 算法原理 • 实现框架（类的层次设计） • 算法实现过程及复杂度分析 • 测试例子及问题等 • Deadline: Wed. 9:30a.m., April 27, 2011

  50. demo

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