Computer Science - The City College of New York
CSC I6716 - Spring 2011 3D Computer Vision
Assignment 2. Edge Detection Exercise ( Due: March 8th
before
class)
Note: Turn in a document (in writing) containing a list of your
.m files, images showing the results of your experiments, and an
analysis of the results.All the writings must be hard copies in print -
you may hand them in during class, or put them in my mailbox in the CS
main office . You also need to
turn in your “soft” copies of your assignment via email.
Send ONLY your source code to me by email – please don’t send in
your
images
and executable (even if you use C++). You are responsible for the
lose
of
your submissions if you don’t write “CSC I6716 Computer Vision
Assignment
2” in the subject of your email. Do write your names and IDs (last four
digits)
in both your hardcopy and softcopy submissions.
Choose one or two of the images on the course web page for this
assignment or use an image of your own choice. If you use a
different image,
be careful that the image has not been saved under JPEG using a high
level
of compression. This often introduces artifacts into the image
that
confound subsequent analyses of the results. For the most part,
you
should apply the edge operators to a grayscale version of the
image.
1. Generate the histogram of the image you are
using. If it is a color image, please first turn it into an
intensity image and then generate its histogram. Try to display
your histogram.
2. Apply the 1x2 operator and Sobel operator to your
image and analyze the results of the gradient magnitude images.
Does the Sobel operator have any
clear visual advantages over the 1x2 operator? If you subtract
the 1x2 edge image from the Sobel
are there any residuals? (Note: don't
forget to normalize your results as shown in slide # 29 of
feature extraction lecture: part 2)
3. Generate edge maps of the above gradient maps. You may
first generate a histogram of each gradient map, and only keep
certain percentage of pixels (e.g. 5% of the highest
gradient values) as edge pixels (edgels) . Use the percentage to
find a threshold for the gradient magnitudes.
4. What happens when you increase the size of the kernel to 5x5 ,
or 7x7? Discuss computational cost (in terms of members of operations,
and the real machine running times), edge detection results and
sensitivity to noise, etc. Note that your larger kernel should still be
an edge detector.
5. Suppose you apply the Sobel operator to each of the RGB color
planes comprising the image. How might you combine these results
into
a color edge detector? Do the resulting edge differ from the gray
scale
results? How and why?