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Image Processing

Image Processing. Example of Digital image. Fine name: F_01.JPG Type of file: JPEG Image Width: 512 Height: 768 Resolution: 96 Bit Depth: 24 Size: 46,744 bytes. Application Fields: Medicine. Image Processing. Application Fields: Geology. Image Processing.

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Image Processing

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  1. Image Processing Example of Digital image Fine name: F_01.JPG Type of file: JPEG Image Width: 512 Height: 768 Resolution: 96 Bit Depth: 24 Size: 46,744 bytes

  2. Application Fields: Medicine Image Processing

  3. Application Fields: Geology Image Processing

  4. Application Fields: Image Enhancement Image Processing

  5. Application Fields: Entertainment Image Processing

  6. Image Processing

  7. Image Processing

  8. Image Processing

  9. Image Processing

  10. x Image Processing y f(x,y) – color value of pixel at (x,y) Digital image is set {f(x,y), for all pixels (x,y)} or { f(x,y), 1≤x ≤W, 1≤y≤H }

  11. Image Processing

  12. Image Processing

  13. Image Processing

  14. Image Processing

  15. Image Processing

  16. Image Processing

  17. Image Processing

  18. Image Processing Gray scale The intensity of a monochrome image at any point (x,y) is called gray level of the image at the point (x,y) : ℓ = f(x, y) The range [Lmin, Lmax], where Lmin  ℓ  Lmax, is called the gray scale.

  19. The set (1) is called 4-neighbors of P. P(x,y) Image Processing Neighbors of a Pixel Let P is a pixel at coordinate (x, y). Then, P has two horizontal and two vertical neighbors at (x+1, y), (x-1, y), (x, y-1), (x, y+1) (1)

  20. The set (1)&(2), denoted by N8(P), is called 8-neighbors of P. P(x,y) Image Processing Neighbors of a Pixel Let P is a pixel at coordinate (x, y). The 4 diagonal neighbors of P: (x-1, y-1), (x-1, y+1), (x+1, y-1), (x+1, y+1) (2)

  21. Image Processing Color Model Color Fundamentals

  22. Image Processing Color Model Color Fundamentals

  23. Image Processing Approximately, 65% of all cones are sensitive to RED light Approximately, 33% are sensitive to GREEN light Color Model Total of 6-7.000.000 cones in the human eye. They are divided into 3 principal sensing categories, corresponding to red, green, blue light. And, approximately, 2% are sensitive to BLUE light (but the blue cones are the most sensitive)

  24. Image Processing Color Model The International Commission on Illumination designated (1931) the following specific wavelength values to the three primary colors: BLUE = 435.8 nm, RED = 700 nm GREEN = 546.1 nm, CIE standards

  25. Image Processing Color Model

  26. Image Processing Color Model

  27. Image Processing Mỗi mô hình màu gồm ba màu, ví dụ: {red, green, blue}, hoặc {cyan, magenta, yellow} Ảnh xám: Mỗi điểm ảnh có các thành phần màu bằng nhau {red = a, green = a, blue= a}, a – mức xám Color Model

  28. Image Processing Color Model

  29. Image Processing Pixels Let P(x, y) is some (discrete) point in the image, and f(P) is vector value of point P, eg., f(P) = [R(P), G(P), B(P)] if I is the color image and, f(P) is called intensity or gray level if I is dark-gray image (image have dark gray-level distributions) The pair P & f(P) is called a pixel (stand for picture element). Usually, used note f(x, y) to sign a pixel, where x, y are spatial coordinates.

  30. Image Processing Pixels Let P(x, y) is some (discrete) point in the image, and f(P) is vector value of point P, eg., f(P) = [R(P), G(P), B(P)] if I is the color image and, f(P) is called intensity or gray level if I is dark-gray image (image have dark gray-level distributions) The pair P & f(P) is called a pixel (stand for picture element). Usually, used note f(x, y) to sign a pixel, where x, y are spatial coordinates.

  31. Một số phép toán xử lý ảnh Lọc nhiễu Nén dữ liệu ảnh

  32. Một số phép toán xử lý ảnh Phân đoạn ảnh

  33. Một số phép toán xử lý ảnh Thủy ấn (Watermarking) An image with visible digital watermarking. The text "Brian Kell 2006" can be seen across the center of the image.

  34. Một số phép toán xử lý ảnh Nhận dạng Who?

  35. Origin image Histogram equalization -brightness +brightness =1.25 =0.9 +contrast -contrast negative bit-plan 5 Biến đổi điểm ảnh Một số phép toán xử lý ảnh

  36. Image Processing BMP File Header Stores general information about the BMP file. Bitmap Information (DIB header)Stores detailed information about the bitmap image. How to practice and what is more reading? A typical BMP file usually contains the following blocks of data: Color PaletteStores the definition of the colors being used for indexed color bitmaps. Bitmap Data Stores the actual image, pixel by pixel.

  37. Image Processing How to practice and what is more reading?

  38. How to practice and what is more reading? Image Processing

  39. Image Processing Note: biBitCount actually specifies the color resolution of the bitmap. The possible values are: 1 (black/white); 4 (16 colors); 8 (256 colors); 24 (16.7 million colors). The biBitCount data element also decides if there is a color table in the file and how it looks like: In 1-bit mode the color table has to contain 2 entries (white, black). In 4-bit mode the color table must contain 16 colors. In 8-bit mode the color table contains 256 entries. In 24-bit mode, the palette is omitted.

  40. The color palette Image Processing

  41. Image Processing Bitmap data: It depens on the BMP File Header structure how the pixel data is to be interpreted. Another important thing is that the number of bytes in one row must always be adjusted to fit into the border of a multiple of four.

  42. In 4-bit mode every byte in the image data represents two pixels. The byte is split into the higher 4 bits and the lower 4 bits and each value of them points to a palette entry. Image Processing In 1-bit mode every byte in the image data represents eight pixels. They are arranged from higher to lower bit for pixels from left to right. In 24-bit mode, three bytes represent one pixel. The first byte represents the red part, the second the green and the third the blue part. Bitmap data: In 8-bit mode every byte represents a pixel. The value points to an entry in the color table. No Color Table

  43. Image Processing • Khái niệm ảnh số. Mô tả ảnh xám, ảnh màu. • Một số bài toán trong xử lý ảnh số. • Một số ứng dụng của xử lý ảnh số. • Các bước chính trong một hệ thống xử lý ảnh. • Sự hình thành ảnh trong mắt người. Quá trình thu nhận ảnh. Số hóa ảnh. • Mô hình màu RGB, HSI và công thức chuyển đổi giữa chúng. • Viết chương trình hiển thị ảnh, thay đổi màu của một số điểm ảnh. • Viết chương trình biến đổi từ ảnh màu (đa sắc) sang ảnh xám (và ngược lại?). Ôn tập, tự học, thực hành:

  44. Image Processing Nguồn tài liệu học tập: http://fit.mta.edu.vn/~tinhdt/cuorsesdetail-8-75.aspx

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