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Image Processing: Noise, Gradients, and Laplacian Operators

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Image Processing: Noise, Gradients, and Laplacian Operators

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Questions and Answers

What is the purpose of non-maximum suppression in the Canny Edge Detector?

To smooth the image

To find peaks in the image gradient (correct)

To find edges in the image

To locate edge strings

What is the advantage of the Canny Edge Detector over other edge detectors?

It has low error rate of detection and well matches human perception results (correct)

It produces double responses at step changes in gray level

It has a high error rate of detection

It produces thicker edges in an image

What is the purpose of hysteresis thresholding in the Canny Edge Detector?

To smooth the image

To locate edge pixels and determine if they form an edge (correct)

To locate edge strings

To find peaks in the image gradient

What is the effect of a first-order derivative on an image?

It has a stronger response to a gray level step Signup and view all the answers

What is the purpose of Gaussian convolution in the Canny Edge Detector?

To smooth the image and reduce noise Signup and view all the answers

What is the purpose of the Laplacian operator?

To detect abrupt changes in an image Signup and view all the answers

What is the result of applying the Laplacian operator to an image?

A zero-crossing map Signup and view all the answers

Why do zero-crossings often result in many false alarms in edge detection?

Because they are sensitive to noise Signup and view all the answers

What is the purpose of the gradient operator?

To detect abrupt changes in an image Signup and view all the answers

What is the difference between the Laplacian operator and the gradient operator?

The Laplacian operator is a second-order derivative, while the gradient operator is a first-order derivative Signup and view all the answers