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JAVA,VC++,opengl,C#, etc. web desgn assignment 代寫

CompSci 373 S1 C 2013 Assignment 3
Due date: 8th June 2013 11.59 pm (Electronic Drop Box)
Goal: implementing image-processing functions with ImageJ.
The assignment focuses on the Week 10 to 13 Lecture Notes, but you are also
encouraged to read more about image processing online.
This assignment is worth 8.33 % of your final mark.   JAVA,VC++,opengl,C#, etc. web desgn assignment 代寫
You must use ImageJ and the skeleton code provided with this assignment. The code
you will add to the current template must be yours i.e. you must not re-use code from
any of the built-in ImageJ functions. Get inspiration from the sample macro provided.
About marking: The markers will need to run your code to assess your assignment.
Make sure to run your ImageJ macros in any of the undergrad lab computers before
submitting. Code must be commented (it will count for 15% of the overall assignment
marking).
Assignment focus: In this assignment you will use images acquired during a CT-
scanning experiment being a part of an international multi-disciplinary project. The
Image Processing component of the project quantified the amount of void in soils that
relates directly to how fast the water permeates through the soil. This is an important
factor in managing soil pollution for farmers around the world. Here, something went
wrong with the industrial CT-scan used and images came out with quite different
intensities for similar material.
This assignment aims on exploring ways to correct such deviations (in order to ensure
that similar materials have similar pixel intensities) and extract the darker pixels,
which correspond to void in the image. You may wish to learn more about CT-scan
imaging (aka X-ray computed tomography) at:

You may also want to learn more about the aforementioned research project at:

e_Processing_for_Environmental_Science.pdf
Important: While the images provided are 16 bits greyscale images, your macros
must also handle 8 bits greyscale images.
Part 1(Question 1): Median filtering [15 marks]
CT-scan images often suffer from “salt and pepper” acquisition noise. To help
removing such noise, you must implement a 9 by 9 median filter using the macro
skeleton code given as Q1. You must use zero padding to process the pixels close to
the image borders. Use your preferred sorting strategy. Make sure to comment
accordingly your code. Use the (smaller) images provided in the assignment test
image folder under Q1.
Part 2 (Question 2): Linear stretching [50 marks]
Q2_a [10 marks]
CT-scan images do not always make the best use of the 16-bits range of pixel
intensities. Here, you will use linear stretching to spread the pixel values better in the
image.
Use the macro skeleton code given as Q2_a to implement linear stretching as per
lecture material. After computing the image histogram H(q) of image I, you will map
the pixel value qmin corresponding to the lowest image intensity to 0 and the pixel
value qmax corresponding to the highest image intensity to the maximum attainable
value (255 for 8 bits greyscale image, 65535 for 16 bits greyscale image).
Q2_b [15 marks]
Spurious pixel intensities of the maximal value that may appear in the image hinder
linear stretching attempted in Q2_a. Here, you will implement ?-β percentile based
mapping by applying the 0.1 – 99.9 percentiles to the active image as per lecture IP1-
Slide 33. You will use the skeleton code Q2_b provided. JAVA,VC++,opengl,C#, etc. web desgn assignment 代寫
Q2_c [25 marks]
The images given have a dominant peak in their histograms. Here, you will use the
position of the dominant peak to align all image features. To do so, open (CTRL+O)
two images to process in ImageJ (the skeleton code provided assumes that you have
already open these two images in ImageJ).
Use the provided skeleton code Q2_c and implement the “peak alignment” procedure.
To do so, consider the two images loaded, say, images I1 and I2, and compute their
respective histograms, H1(q) and H2(q). Transform I2 by linear stretching that maps
q2min (the pixel value corresponding to the lowest intensity in I2) to q1min (the pixel
value corresponding to the lowest intensity in I1) and q2peak (the pixel value for the
maximum count, H2(q2peak) = max{q} H2(q), in the histogram H2(q)) to q1peak (the
pixel value for the maximum count in the histogram H1(q)).
Part 3 (Question 3): Image segmenting (iterative adaptive threshold) [35 marks]
The images provided for Q2 and Q3 clearly have 4 regions: the region R0 outside the
plastic container with pixel value 0, the plastic container region Rpc (lighter pixel
values above some threshold, θ2), the soil region Rs (grey to light grey pixel values
between the thresholds θ1 and θ2) and the soil pores, or soil void region Rp (darker
pixel values between 1 and θ1). See slide 5 of the notes located here:

e_Processing_for_Environmental_Science.pdf
Use the macro skeleton code given as Q3 to segment greyscale images by modified
adaptive threshold approach. Here, the algorithm presented in Lecture Notes IP02,
Slides 32 to 36, is modified as follows. Given an image I, associated histogram H(q),
and the maximum pixel value nBins allowed for image I, find the thresholds θ1 and θ2
by performing the following operations:
1. Initialize the thresholds: θ1 = qpeak/2 and θ2 = (qmax + qpeak)/2 where qpeak is the
pixel value for the maximum histogram count H(q) and qmax is the pixel value
corresponding to the largest intensity in the image I.
2. Update θ1 and θ2 until their values do not evolve or you reach the maximum
number of iterations required (MAX_ITER = 20):
Do (up to 20 times): following Lecture Notes IP2, Slide 35,
a. Update θ1 considering pixels of the region Rp as object and pixels of the
region Rs as background.
b. Update θ2 considering pixels of the region Rs as object and pixels Rpc
region as background. JAVA,VC++,opengl,C#, etc. web desgn assignment 代寫

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