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Sample Assignment
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.
Q2. Calculate the value of the blue patch in Fig. 2 using bilateral filtering. Assume the Domain kernel is of size 5 × 5, the standard deviation σd = 2, provided as
the 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 marks).
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 1 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 from wattle (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.
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.
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 perpendicular 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.
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.
Deliverables You Can Expect
At CodersArts, we provide more than just assistance — we deliver the complete code for your image processing assignment. Our commitment extends to ensuring you not only receive the code artifacts but also a basic detailing report outlining the workflow of the code.
How We Can Help You Overcome Challenges
CodersArts offers customized solutions to conquer the complexities of this assignment:
Expert Image Processing Guidance: Benefit from the expertise of our seasoned professionals in image processing as they guide you through each step of the assignment.
Efficient Bilateral Filtering: Learn techniques to efficiently perform bilateral filtering, ensuring accurate results for the specified image patch.
Contour Detection Mastery: Gain a deep understanding of contour detection through our step-by-step instructions, addressing common challenges and enhancing the overall quality of the detection.
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