Microsoft Word – COMP_ENGN6528_Computer_Vision_Assignment_2023.docx
Q1. The following is a separable filter. What does it mean to be a separable filter?(0.5 mark) Write down the
separate components of the following filter. (1 marks)
Figure 1: Separable Filter
Q2. Calculate the value of the blue patch in Fig. 2 using bilateral filtering. Assume the Domain kernel is
0.0232 0.0338 0.0383 0.0338 0.0232
0.0338 0.0492 0.0558 0.0492 0.0338
of size 5 × 5, the standard deviation σd = 2, provided as 0.0383 0.0558 0.0632 0.0558 0.0383
0.0338 0.0492 0.0558 0.0492 0.0338
0.0232 0.0338 0.0383 0.0338 0.0232
Range kernel is of size 5 × 5 and the standard deviation σr = 50. Please 1) provide the range filter associated
to the pixel high-lighted in the Fig. 2 (2 marks), and 2) show the filtered value for the high-lighted pixel (2
Figure 2: Image Patch
Q3. Contour Detection [10 marks+5 marks(extra)]
Acknowledgement: Lab material with code are adapted from the one by Professor Saurabh Gupta from
UIUC, copyright by UIUC.
In this problem we will build a basic contour detector. We will work with some images from the BSDS
dataset (see [1]), and benchmark the performance of our contour detector against human annotations. You
can review the basic concepts from lecture on edge detection (Week 3). We will generate a per-pixel boundary
score. We will start from a very simple edge detector that simply uses the gradient magnitude of each pixel
as the boundary score. We will add non-maximum suppression, image smoothing, and optionally additional
bells and whistles. We have provided some starter code, images from the BSDS dataset and the evaluation
code. Note that we are using a faster approximate version of the evaluation code, so metrics here won’t be
directly comparable to ones reported in papers.
Preliminaries. Download the starter code, images and evaluation code (Assignment.zip)(see contour–data,
contour demo.py/contour demo.m). The code has implemented a contour detector that uses the magnitude
of the local image gradient as the boundary score. This gives us overall max F-score, average max F-score and
AP (average precision) of 0.51, 0.56, 0.41 respectively. Reproduce these results by running contour
demo.py/contour demo.m. Confirm your setup by matching these results. Note that the matlab version may
be with 0.01difference from the python code due to the difference in inbuilt functions.
When you run contour demo.py/contour demo.m, it saves the output contours in the folder outputdemo,
prints out the 3 metrics, and produces a precision–recall plots at contour–output/demo pr.pdf. Overall max
F–score is the most important metric, but we will look at all three.
• Warm-up. As you visualize the produced edges, you will notice artifacts at image boundaries. Modify
how the convolution is being done to minimize these artifacts. (1 mark)
• Smoothing. Next, notice that we are using [−1, 0, 1] filters for computing the gradients, and they are
susceptible to noise. Use derivative of Gaussian filters to obtain more robust estimates of the gradient.
Experiment with different sigma for this Gaussian filtering and pick the one that works the best. (3
• Non-maximum Suppression. The current code does not produce thin edges. Implement non- maximum
suppression, where we look at the gradient magnitude at the two neighbours in the direction perpen-
dicular to the edge. We suppress the output at the current pixel if the output at the current pixel is
not more than at the neighbors. You will have to compute the orientation of the contour (using the X
and Y gradients), and implement interpolation to lookup values at the neighbouring pixels. (6 marks)
In the code, you may need to define your own edge detector with non-maximum suppression. Note
that all the functions are called in ‘detect edges()’.
• Extra Credit. You should implement other modifications to get this contour detector to work even better.
Here are some suggestions: compute edges at multiple different scales, use color information, propagate
strength along a contiguous contour, etc. You are welcome to read and implement ideas from
papers on this topic. (upto 5 marks)
For each of the modifications above, your report should include:
• key implementation details focusing on the non-trivial aspects, hyper-parameters that worked the best,
• contour quality performance metrics before and after the modification.
• impact of modification on run-time,
• visual examples that show the effects of the modification on two to three images.
————-This is the end of the assignment. ————–