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![Assignment 1
Implement a modified Canny edge detector:
edge enhancement
Apply Gaussian smoothing to the image. The kernel size of the Gaussian filter is the first
user input. Use any method to compute the gradient of the smoothed image and estimate
the magnitude and orientation of the gradient. For each pixel, find the best gradient
orientation out of 4 possible directions - 0°, 45°, 90°, and 135° with respect to the
horizontal axis. Show the gradient orientation in the following way:
0° - black
45° - dark grey
90° - light grey
135° - white
Also, show the gradient magnitude by quantizing to 256 grey levels.
non-maximum suppression
If the gradient magnitude is smaller than at least one of its two neighbors along the best
gradient orientation, change it to zero. Show the non-maximum suppressed gradient
magnitude by quantizing to 256 grey levels.
hysteresis thresholding
The two thresholds τl and τh are the second and third user inputs. If the suppressed
gradient magnitude is larger than τh, locate it as an edge pixel. If it is not an edge pixel,
check its two neighbors along the perpendicular direction to the best gradient orientation.
If they have gradient magnitude larger than τl, locate them as edge pixels. Continue to
examine the whole image to locate all edge pixels. Show the final result – edge pixels as
white, non-edge pixels as black.
Make sure you have added comments to the source code. If you are not using MATLAB,
please also submit the executable file. Include the source code, intermediate results and
the final result into a MS Word file. Indicate the setting of your program. Use the test
image as provided in CityU Blackboard. Submit the MS Word file by e-mail on or before
2 March 2012.
[10 marks]](https://image.slidesharecdn.com/test2968-120329135629-phpapp01/85/Test-1-320.jpg)
![Assignment 1
Implement a modified Canny edge detector:
edge enhancement
Apply Gaussian smoothing to the image. The kernel size of the Gaussian filter is the first
user input. Use any method to compute the gradient of the smoothed image and estimate
the magnitude and orientation of the gradient. For each pixel, find the best gradient
orientation out of 4 possible directions - 0°, 45°, 90°, and 135° with respect to the
horizontal axis. Show the gradient orientation in the following way:
0° - black
45° - dark grey
90° - light grey
135° - white
Also, show the gradient magnitude by quantizing to 256 grey levels.
non-maximum suppression
If the gradient magnitude is smaller than at least one of its two neighbors along the best
gradient orientation, change it to zero. Show the non-maximum suppressed gradient
magnitude by quantizing to 256 grey levels.
hysteresis thresholding
The two thresholds τl and τh are the second and third user inputs. If the suppressed
gradient magnitude is larger than τh, locate it as an edge pixel. If it is not an edge pixel,
check its two neighbors along the perpendicular direction to the best gradient orientation.
If they have gradient magnitude larger than τl, locate them as edge pixels. Continue to
examine the whole image to locate all edge pixels. Show the final result – edge pixels as
white, non-edge pixels as black.
Make sure you have added comments to the source code. If you are not using MATLAB,
please also submit the executable file. Include the source code, intermediate results and
the final result into a MS Word file. Indicate the setting of your program. Use the test
image as provided in CityU Blackboard. Submit the MS Word file by e-mail on or before
2 March 2012.
[10 marks]](https://image.slidesharecdn.com/test2968-120329135629-phpapp01/75/Test-1-2048.jpg)
Uploaded byKinni MEW
Test
The document provides instructions for implementing a modified Canny edge detector with three user inputs: the Gaussian kernel size, and two threshold values τl and τh. The algorithm involves edge enhancement by applying Gaussian smoothing and computing gradients, non-maximum suppression to remove non-edges, and hysteresis thresholding to determine final edge pixels using the two threshold values. The output should show edge pixels in white and non-edges in black, with source code, intermediate results, and settings documented in a Word file.
Test
- 1. Assignment 1 Implement amodified Canny edge detector: edge enhancement Apply Gaussian smoothing to the image. The kernel size of the Gaussian filter is the first user input. Use any method to compute the gradient of the smoothed image and estimate the magnitude and orientation of the gradient. For each pixel, find the best gradient orientation out of 4 possible directions - 0°, 45°, 90°, and 135° with respect to the horizontal axis. Show the gradient orientation in the following way: 0° - black 45° - dark grey 90° - light grey 135° - white Also, show the gradient magnitude by quantizing to 256 grey levels. non-maximum suppression If the gradient magnitude is smaller than at least one of its two neighbors along the best gradient orientation, change it to zero. Show the non-maximum suppressed gradient magnitude by quantizing to 256 grey levels. hysteresis thresholding The two thresholds τl and τh are the second and third user inputs. If the suppressed gradient magnitude is larger than τh, locate it as an edge pixel. If it is not an edge pixel, check its two neighbors along the perpendicular direction to the best gradient orientation. If they have gradient magnitude larger than τl, locate them as edge pixels. Continue to examine the whole image to locate all edge pixels. Show the final result – edge pixels as white, non-edge pixels as black. Make sure you have added comments to the source code. If you are not using MATLAB, please also submit the executable file. Include the source code, intermediate results and the final result into a MS Word file. Indicate the setting of your program. Use the test image as provided in CityU Blackboard. Submit the MS Word file by e-mail on or before 2 March 2012. [10 marks]