Description
1. (20 points) Generate the histogram of the image you are using, and then perform a number of
histogram operations (such as contrast enhancement, thresholding and equalization) to make
the image visually better for either viewing or processing (10 points). If it is a color image,
please first turn it into an intensity image and then generate its histogram. Try to display your
histogram (5 points), and make some observations of the image based on its histogram (5
points). What are the general distributions of the intensity values? How many major peaks and
valleys does your histogram have? How could you use the histogram to understand, analyze or
segment the image? Please also display the histograms of the processed images and provide a
few important observations.
2. (20 points) Apply the 1×2 operator and Sobel operator to your image and analyze the results
of the gradient magnitude images (including vertical gradients, horizontal gradients, and the
combined) (10 points). Please don’t forget to normalize your gradient images, noting that the
original vertical and horizontal gradients have both positive and negative values. I would
recommend you to display the absolute values of the horizontal and vertical gradient images.
Does the Sobel operator have any clear visual advantages over the 1×2 operator? Any
disadvantages (5 points)? If you subtract the 1×2 edge image from the Sobel are there any
residuals? You might use two different types of images: one ideal man-made image, and one
image of a real scene with more details (5 points). (Note: don’t forget to normalize your
results as shown in slide # 29 of feature extraction lecture: part 2)
3. (20 points) Generate edge maps of the above two combined gradient maps (10 points). An
edge image should be a binary image with 1s as edge points and 0s as non-edge points. You
may first generate a histogram of each gradient map, and only keep certain percentage of
pixels (e.g. 5% of the pixels with the highest gradient values) as edge pixels (edgels) . Use the
percentage to automatically find a threshold for the gradient magnitudes. In your report, please
write up the description and probably equations for finding the threshold, and discuss if 5% is a
good value. If not what is (5 points) ? You may also consider to use local, adaptive thresholds to
different portions of the image so that all major edges will be shown up nicely (5 points). In the
end, please try to generate a sketch of an image, such as the ID image of Prof. Zhu.
4. (20 points) What happens when you increase the size of the edge detection kernel from 1×2
to 3×3 and then to 5×5 , or 7×7? Discuss computational cost (in terms of members of operations,
and the real machine running times – 5 points), edge detection results (5 points) and sensitivity
to noise, etc. (5 points). Note that your larger kernel should still be an edge detector. Please list
your kernels as matrices in your report, and tell us what they are good for (5 points).
5. (20 points) Suppose you apply the Sobel operator to each of the RGB color bands of a color
image. How might you combine these results into a color edge detector (5 points)? Do the
resulting edge differ from the gray scale results? How and why (5 points)? You may compare
the edge maps of the intensity image (of the color image), the gray-scale edge map that are the
combination of the three edge maps from three color bands, or a real color edge map that edge
points have colors (5 points). Please discuss their similarities and differences, and how each of
them can be used for image enhancement or feature extraction (5 points). Note that you want to
first generate gradient maps and then using thresholding to generate edge maps. In the end,
please try to generate a color sketch of an image, such as the ID image of Prof. Zhu. You may
also consider local, adaptive thresholding in generating a color edge map.
Computer Science – The City College of New York
