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关于图像识别:CS-659图像处理

CS 659 Image Processing
Sample Midterm Exam (Closed Book, 90 minutes)
Covering Lectures 1~6. There are 5 questions. Each is 20 points.

  1. (20 points)
    (a) Let a grayscale image be the size 100-by-200 with 256 gray levels. How many bits in
    memory are required to store this grayscale image without adding the overhead?
    (b) Let a color image be the size 100-by-200. How many bits in memory are required to store
    this color image without adding the overhead?
    (c) If we reduce the image size by a half in both row and column. What fraction in the memory
    size can we reduce?
    Answer:
    (a) 1002008=160,000 bits
    (b) 1002008*3=480,000 bits
    (c) or 0.25
  2. (20 points) Let an image f of 4-by-4 and a mask g of 2-by-2 as follows. Calculate the
    convolution f * g and correlation f ° g. Note the origin is located at lower-left corner.
  3. (20 points)
    (a) Let an image of 2-by-2 as follows. Perform image negative on this image. What is the
    resulting image?
  4. 33
  5. 255
    (b) Let an image of 2-by-2 as follows. Perform bit-plane slicing on this image. What are the eight
    images on each bit-plane, B0, B1, B2, B3, B4, B5, B6, and B7, from Least Significant Bit (LSB)
    B0 to Most Significant Bit (MSB) B7?
  6. 33
  7. 255
    Answer:
    (a) We use 255 to subtract each pixel.
  8. 222
  9. 0
    (b) We represent each pixel into 8-bit as:
    3=00000011,
    33=00100001
    170=10101010
    255=11111111
    Therefore,
  10. (20 points)
    Let a binary image be f and a template be g as follows. Perform image matching using the
    equation: Note that is correlation and
    is the complement. What is the
    resulting image?
    1
  11. (20 points) Explain the image equalization and image specification. Briefly describe both
    procedures to achieve their goals.
    Answer:
    In histogram equalization we are trying to maximize the image contrast by applying a gray level
    transform which tries to flatten the resulting histogram. It turns out that the gray level transform
    that we are seeking is simply a scaled version of the original image’s cumulative histogram. That
    is, the graylevel transform T is given by T[i] = (G-1)c(i), where G is the number of gray levels
    and c(i) is the normalized cumulative histogram of the original image.
    r: the gray levels to be enhanced.
    Assume r continuous in [0,1]
    s=T(r)
    a) T(r) single-valued, monotonically increasing
    b) 0<=T(r)<=1
    When we want to specify a non-flat resulting histogram, we can use the following steps (called
    image specification):
  12. Specify the desired histogram g(z)
  13. Obtain the transform which would equalize the specified histogram, Tg, and its inverse
    Tg-1
  14. Get the transform which would histogram equalize the original image, s=T[i]
  15. Apply the inverse transform Tg-1
    on the equalized image, that is z=Tg-1
    WX:codehelp
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