|Distance transform of binary image|
|Geodesic distance transform of binary image|
|Gray-weighted distance transform of grayscale image|
|Extract line segments based on Hough transform|
|Identify peaks in Hough transform|
|2-D discrete cosine transform|
|Discrete cosine transform matrix|
|Convert fan-beam projections to parallel-beam|
|2-D inverse discrete cosine transform|
|Inverse fan-beam transform|
|Inverse Radon transform|
|Convert parallel-beam projections to fan-beam|
|2-D fast Fourier transform|
|Shift zero-frequency component to center of spectrum|
|2-D inverse fast Fourier transform|
|Inverse zero-frequency shift|
Learn about the Fourier transform and some of its applications in image processing, particularly in image filtering.
Learn about the discrete cosine transform (DCT) of an image and its applications, particularly in image compression.
The Hough transform detects lines in an image, including lines tilted at arbitrary angles from vertical and horizontal. The Hough transform tends to be quick, but can exhibit artifacts.
The Radon transform detects lines in an image, including lines tilted at arbitrary angles from vertical and horizontal. The Radon transform tends to be more accurate at the cost of longer computation time.
The inverse Radon transform reconstructs an image from a set of parallel-beam projection data across many projection angles.
This example shows how to detect lines and identify the strongest lines in an image using the Radon transform.
Use fan-beam projection and reconstruction when projections of an image are acquired along paths radiating from a point source. Medical tomography is a common application of fan-beam projection.