Description
Problem 1: Edge Detection (50 %)
(a) Sobel Edge Detector (10%)
Implement the Sobel edge detector and apply to Tiger.raw and Pig.raw images as shown in Fig. 1 (a) and
(b). Note that you need to convert RGB images to grayscale image using the formula below.
= 0.2989 ∗ + 0.5870 ∗ + 0.1140 ∗
Include the following in your results:


Normalize the xgradient and the ygradient values to 0255 respectively and show the results;



Calculate and show the normalized gradient magnitude map;



Tune the thresholds (in terms of percentage) to obtain an edge map with the best visual performance. The final edge map should be a binary image whose pixel values are either 0 (edge) or 255 (background).


Canny Edge Detector (10%)
The Canny edge detector is an edge detection technique utilizing image’s intensity gradients and nonmaximum suppression with double thresholding. In this part, apply the Canny edge detector [1] to both Tiger.jpg and Pig.jpg images from BSDS 500 dataset [2]. You are allowed to use any online source code such as the Canny edge detector in the MATLAB image processing toolbox or OpenCV.

Explain Nonmaximum suppression in Canny edge detector in your own words.

How are high and low threshold values used in Canny edge detector?

Generate edge maps by trying different low and high thresholds and discuss your results.
Figure 1: Tiger and Pig images
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Structured Edge (15%)
Apply the Structured Edge (SE) detector [3] to extract edge segments from a color image with online source codes (released toolbox in MATLAB: https://github.com/pdollar/edges). Exemplary edge maps generated by the SE method for the Boat image from BSDS 500 are shown in Figure 2. You can apply the SE detector to the RGB image directly without converting it into a grayscale image. Also, the SE detector will generate a probability edge map. To obtain a binary edge map, you need to binarize the probability edge map with a threshold.

Please digest the SE detection algorithm. Summarize it with a flow chart and explain it in your own words (no more than 1 page, including both the flow chart and your explanation).

Random Forest (RF) classifier is used in the SE detector. The RF classifier consists of multiple decision trees and integrate the results of these decision trees into one final probability function. Explain the process of decision tree construction and the principle of the RF classifier.

Apply the SE detector to Tiger.jpg and Pig.jpg images. State the chosen parameters clearly and justify your selection. Compare and comment on the visual results of the Canny detector and the SE detector.
Boat Probability edge map Binary edge map (with p>0.8)
Figure 2: The Boat image and its probability and binary edge maps obtained by the Structured Edge detector
(d) Performance Evaluation (15%)
Perform quantitative comparison between different edge maps obtained by different edge detectors. The ultimate goal of edge detection is to enable the machine to generate contours of priority to human being. For this reason, we need the edge map provided by human (called the ground truth) to evaluate the quality of a machinegenerated edge map. However, different people may have different opinions about the importance of different edges in an image. To handle the opinion diversity, it is typical to take the mean of a certain performance measure with respect to each ground truth, e.g. the mean precision, the mean recall, etc. Figure 3 shows the 6 ground truth edge maps for the Boat image. To evaluate the performance of a generated edge map, we need to identify the error. Every pixel in a generated edge map will be belong to either of the following four classes:

True positive: Edge pixels in the edge map coincide with edge pixels in the ground truth. These are edge pixels the algorithm successfully identifies.

True negative: Nonedge pixels in the edge map coincide with nonedge pixels in the ground truth. These are nonedge pixels the algorithm successfully identifies.
569 Digital Image Processing: Homework #2

False positive: Edge pixels in the edge map correspond to the nonedge pixels in the ground truth. These are fake edge pixels the algorithm wrongly identifies.

False negative: Nonedge pixels in the edge map correspond to the true edge pixels in the ground truth. These are edge pixels the algorithm misses.
Clearly, pixels in (i) and (ii) are correct ones while those in (iii) and (iv) are error pixels of two different types. The performance of an edge detection algorithm can be measured using the F measure, which is a function of the precision and the recall.

Calculate the precision (P) and recall (R) for each GT (provided in .mat format) separately using the function provided by SE software package. Compute mean P and mean R for each GT. Average all mean P and mean R to get overall mean precision and overall mean recall for all GT. Finally, calculate the F measure (scalar) from overall mean precision and overall mean recall. Please use a table to show the precision and recall for each ground truth, their means and the final F measure (scalar). Comment on the performance of different edge detectors (i.e. pros and cons.)

Similar to (1), compute the mean P and mean R for each threshold across all GT. For each threshold, compute mean F measure. Plot the curve of how F measure changes by threshold. Find the best F measure and discuss the plot for different edge detectors.

The F measure is image dependent. Which image is easier to a get high F measure – Tiger or Pig? Please provide an intuitive explanation to your answer.

Discuss the rationale behind the F measure definition. Is it possible to get a high F measure if precision is significantly higher than recall, or vice versa? If the sum of precision and recall is a constant, show that the F measure reaches the maximum when precision is equal to recall.
Problem 2: Digital Halftoning (30%)
(a) Dithering (15%)
Figure 4 Bridge is a grayscale image. Implement the following methods to convert it to halftoned images. In the following discussion, ( , ) and ( , ) denote the pixel at position ( , ) in the input and output images respectively. Show and compare the results obtained by the following algorithms in your report.
Figure 4: Bridge ^{[4]}
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EE 569 Digital Image Processing: Homework #2
1. Fixed thresholding
Choose one value, T, as the threshold to divide the 256 intensity levels into two ranges. An intuitive choice of T would be 128. For each pixel, map it to 0 if it is smaller than T, otherwise, map it to 255, i.e.






0
if 0 ≤ ( , ) <
( , ) = {_{255}
if ≤ ( , ) < 256





2. Random thresholding
In order to break the monotones in the result from fixed thresholding, we may use a random threshold.
The algorithm can be described as:

For each pixel ( , ), generate a random number in the range 0 ∼ 255, so called ( , )

Compare the pixel value with ( , ). If the pixel value is greater, then map it to 255; otherwise,
map it to 0, i.e.





( , ) = {
0
0 ≤ ( , ) < ( , )
255
( , ) ≤ ( , ) < 256




A choice of random threshold could be from uniformly distributed random variables. Check your coding language documentation for proper random variable generator.
3. Dithering Matrix
Dithering parameters are specified by an index matrix. The values in an index matrix indicate how likely a dot will be turned on. For example, an index matrix is given by








_{2}
( , ) = [^{1}
2_{]}
3
0







where 0 indicates the pixel that is the most likely to be turned on, and 3 is the least likely one. This index matrix is a special case of a family of dithering matrices firstly introduced by Bayer [5]. The Bayer index matrices are defined recursively using the formula:





4 × _{n}( , ) + 1
4 × _{n}( , ) + 2
_{2n}( , ) = [
4 × _{n}( , )
]
4 × _{n}( , ) + 3




The index matrix can then be transformed into a threshold matrix T for an input grayscale image with normalized pixel values (i.e. with its dynamic range between 0 and 255) by the following formula:
_{ ( , ) =} ^{( , ) + 0.5 }_{× 255}
^{2}
where ^{2} denotes the total number of pixels in the threshold matrix, and ( , ) is the location in the matrix. Since the image is usually much larger than the threshold matrix, the matrix is repeated periodically across the full image. This is done by using the following formula:
_{ ( , ) = { }0 ( , ) ≤ ( , )
255 ℎ
Please create _{2}, _{8}, _{32} threshold matrices and apply them to halftone Bridge image (Fig. 4). Show each of your threshold matrix and your corresponding dithered image. Compare your results and discuss the difference.

Error Diffusion (15%)
Convert the 8bit Bridge image to a halftoned one using the error diffusion method. Show the outputs of the following three variations and discuss these obtained results. Compare these results with Dithering Matrix method in part (a). Which method do you prefer? Why?

FloydSteinberg’s error diffusion with the serpentine scanning, where the error diffusion matrix is:








1
0
0
0
[0
0
7]
16
3
5
1







(2) Error diffusion proposed by Jarvis, Judice, and Ninke (JJN), where the error diffusion matrix is:







0
0
0
0
0
1
0
0
0
0
0
0
0
0
7
5
48
3
5
7
5
3
^{[}1
3
5
3
1^{]}






(3) Error diffusion proposed by Stucki, where the error diffusion matrix is:







0
0
0
0
0
1
0
0
0
0
0
0
0
0
8
4
42
2
4
8
4
2
[_{1}
2
4
2
1^{]}






Describe your own idea to get better results. You don’t need to implement your idea if the time is limited.
Please explain why your proposed method will lead to better results.
Problem 3: Color Halftoning with Error Diffusion (20%)
Figure 5: The Bird image ^{[6]}

Separable Error Diffusion (4%)
One simple idea to achieve color halftoning is to separate an image into CMY three channels and apply the FloydSteinberg error diffusion algorithm to quantize each channel separately. Then, you will have one of the following 8 colors, which correspond to the 8 vertices of the CMY cube at each pixel:
W = (0,0,0), Y = (0,0,1), C = (1,0,0), M = (0,1,0),
G = (1,0,1), R = (0,1,1), B = (1,1,0), K = (1,1,1)
Note that (W, K), (Y, B), (C, R), (M, G) are complementary color pairs. Please show and discuss the result of the halftoned color Bird image. What is the main shortcoming of this approach?
(b) MBVQbased Error diffusion (16%)
Shaked et al. [7] proposed a new error diffusion method, which can overcome the shortcoming of the separable error diffusion method. They partition the RGB color space into six Minimum Brightness Variation Quadrants (MBVQ) as shown in Fig. 2 of [7]. Then, the MBVQbased error diffusion can be conducted as follows. Given the RGB value and the accumulated quantization error at a pixel located in (x, y). They are denoted by RGB(x, y) and e(x, y), respectively.

Determine its MBVQ quadrant based on RGB (x, y);

Find the vertex V of the MBVQ tetrahedron to closest RGB (x, y) + e (x, y) and output the value of V at location (x, y);

Compute the new quantization error using RGB (x, y) + e (x, y) – V

Distribute the quantized error to the future pixel error buckets e using a standard error diffusion process (e.g. the FS error diffusion).
Please read [7] carefully, and answer the following questions:

Describe the key ideas on which the MBVQbased Error diffusion method is established and give reasons why this method can overcome the shortcoming of the separable error diffusion method.

Implement the algorithm using a standard error diffusion process (e.g. the FloydSteinberg error diffusion) and apply it to Fig. 5. Compare the output with that obtained by the separable error diffusion method.
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