Active contours without edges - help

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In summary: Additionally, it is crucial to use the correct force information derived from the image or external sources in order to achieve an accurate evolution of phi. Overall, understanding the basics of active contours without edges and correctly applying the forces will allow for successful evolution of phi. In summary, the evolution of phi along the level set in active contours without edges involves applying forces to the contour's points using an equation with a timestep and correct force information from the image or external sources. The elements of phi are not multiplied, but added together to compute the new position of the contour.
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physical101
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Hi there I have been investigating active contours without edges recently and have become stuck with the evolution of phi along the level set. I understand most of the basics but was wondering if anyone who is familar with active contours without edges could tell me how to evolve phi. Currently I am using the pixel coordinates of phi and then adding a timestep multiplied by force information derived from the image to move get new coordinate info for the contour. Is this correct? Are the elements of phi supposed to multiplied?
Any help will be much appreciated
Physical 101
 
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  • #2
The evolution of phi along the level set is based on the concept of deformable contours. Essentially, a contour (or curve) is deformed by the application of forces to its points. The forces can be derived from the image itself or from other external sources. The application of these forces is calculated using the equation: \phi(x,t+1)=\phi(x,t)+\Delta t \cdot F(x,t)Where \phi(x,t) represents the coordinates of the contour at time t and F(x,t) is the force applied to the contour at that time. The timestep \Delta t is a small constant used to calculate the amount of displacement of the contour due to the applied force.In order to evolve phi along the level set, you must iteratively apply the above equation for each point on the contour. It is important to note that the elements of phi are not multiplied, but instead added together in order to compute the new position of the contour.
 

Related to Active contours without edges - help

What is the concept of "Active contours without edges"?

Active contours without edges is a computer vision technique used for image segmentation. It involves finding the boundaries of objects in an image without relying on edge detection, which can be sensitive to noise and variations in lighting. Instead, it uses a combination of energy minimization and curve evolution to detect and segment objects in an image.

How does "Active contours without edges" work?

The technique of active contours without edges involves initializing a curve or contour within an image and then using energy minimization techniques to evolve the contour to fit the boundaries of objects in the image. This is done by minimizing an energy functional that takes into account both the internal energy of the contour and the image information, such as intensity gradients and texture.

What are the advantages of using "Active contours without edges"?

"Active contours without edges" has several advantages over traditional edge detection techniques. It is more robust to noise and variations in lighting, as it does not rely on detecting edges. It also has the ability to segment objects with irregular or weak boundaries, making it useful in a variety of applications such as medical imaging, object tracking, and image enhancement.

What are the limitations of "Active contours without edges"?

While "Active contours without edges" has many advantages, it also has some limitations. It can struggle with complex object boundaries or images with multiple objects overlapping. It also requires careful initialization and parameter tuning, which can be time-consuming and challenging for certain applications.

How is "Active contours without edges" used in real-world applications?

"Active contours without edges" has a wide range of applications, including medical imaging, object tracking, and image enhancement. In medical imaging, it can be used to segment different tissues or organs in images, aiding in diagnosis and treatment planning. It is also used in object tracking to detect and track objects in videos. In image enhancement, it can be used to remove noise and improve the overall quality of an image.

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