- Image Blending
- Histogram Equalization
- Gaussian Filtering
- Median Filtering
- Bilinear Interpolation
- Edge Detection (Sobel operator)
- Edge Detection (Marr–Hildreth algorithm)
- Feature Matching
- Implement a function that creates a composite image from two images, im1 and im2.
- The function has three inputs (im1, im2, α) and one output (result).
- result = α⋅im1+(1−α)⋅im2!
- Implement a function that processes histogram equalization.
- The function has two inputs (im, n) and one output (result).
- “n” is the number of levels of the histogram.
- Implement a function that processes Gaussian filtering on an image using a Gaussian filter.
- The function has two inputs (im, σ) and one output (result).
- σ: standard deviation of Gaussian filter
- The size of filter: (2×⌈2σ⌉+1 by 2×⌈2σ⌉+1)
- For boundary region, if a pixel is out of image, assume the pixel has the same intensity with the closest pixel
- Implement a function that processes median filtering on an image.
- The function has two inputs (im, filterSize) and one output (result).
- For boundary region, if a pixel is out of image, assume the pixel has the same intensity with the closest pixel.
- Implement a function that processes bilinear interpolation to increase the size of an image.
- The function has two inputs (im, scale) and one output (result).
- For elements at right-end and bottom-end, assume the pixel has the same intensity with the closest pixel.
- Implement a function that detects edges using Sobel operator on an image.
- The function has one input (im) and one output (result).
- The output of the function should be the magnitude of the gradient of an input image.
- For boundary region, if a pixel is out of image, assume the pixel has the same intensity with the closest pixel.
- Implement a function that detects edges using the Marr–Hildreth algorithm on an image.
- The function has three inputs (im, σ, threshold) and one output (result).
- The output of the function should be a binary edge map.
- σ: standard deviation of Gaussian filter.
- threshold: threshold for zero-crossing.
- Size of LoG filter: (2×⌈3σ⌉+1 by 2×⌈3σ⌉+1)
- For boundary region, if a pixel is out of image, assume the pixel has the same intensity with the closest pixel.
- Implement a function “detectHarrisCorner” that detects interest points using the Harris corner detection algorithm on an image.
- The function has seven inputs (im, fx_operator, fy_operator, Gaussian_sigma, alpha, C_thres, NMS_ws) and two outputs (corner, C).
- The output “corner” should contain [x, y] coordinates of interest points.
- The output “C” should contain a cornerness score map which has the same size as the input image.
- im: input image.
- fx_operator, fy_operator: vector to use to compute derivatives along x-direction and y-direction.
- Gaussian_sigma: standard deviation of Gaussian filter for filtering squares/products of the derivative maps.
- alpha: for the cornerness score map.
- C_thres: threshold for the cornerness score map.
- NMS_ws: window size for non-maximum suppression.
- Size of Gaussian filter: (2×⌈2σ⌉+1 by 2×⌈2σ⌉+1).
- For boundary region, ignore interest points (in 8 pixels from any boundary).
The cornerness of the two images
The detected corner of the two images
- Implement a function “extractSIFT” that extracts SIFT features using the Scale Invariant Feature Transform (SIFT) algorithm around interest points.
- The function has five inputs (im, fx_operator, fy_operator, corner, Gaussian_sigma) and one output (SIFT).
- The output “SIFT” should contain SIFT features for all “corner” points. (size: # of corner X 128) image.
- im: input image.
- fx_operator, fy_operator: vector to use to compute derivatives along x-direction and y-direction.
- corner: from “detectHarrisCorner”.
- Gaussian_sigma: standard deviation of the Gaussian filter for filtering the magnitude of the gradient.
- Size of Gaussian filter: (16×16).
- For boundary region, ignore interest points (in 8 pixels from any boundary).
- Implement a function “matchFeatures” that matches SIFT features using the nearest neighbor distance ratio algorithm between two images.
- The function has two inputs (SIFT1, SIFT2) and one output (matching).
- The output “matching” should contain two columns. The first column should contain the index of matched interest points from image 2. The second column should contain distance ratios. (size: # of corners in SIFT1 X 2)
- SIFT1, SIFT2: from “extractSIFT”.
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