Q. “For very large value of A, a high boost filtered image is approximately equal to the original image”. State whether the statement is true or false?

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Q. Subtracting Laplacian from an image is proportional to which of the following?

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Q. A First derivative in image processing is implemented using which of the following given operator(s)?

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Q. What is the sum of the coefficient of the mask defined using gradient?

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Q. Gradient is used in which of the following area(s)?

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Q. Gradient have some important features. Which of the following is/are some of them?

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Q. An image has significant edge details. Which of the following fact(s) is/are true for the gradient image and the Laplacian image of the same?

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Q. The Laplacian in frequency domain is simply implemented by using filter __________

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Q. Assuming that the origin of F(u, v), Fourier transformed function of f(x, y) an input image, has been correlated by performing the operation f(x, y)(-1)x+y prior to taking the transform of the image. If F and f are of same size, then what does the given operation is/are supposed to do?

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Q. Assuming that the origin of F(u, v), Fourier transformed function of f(x, y) an input image, has been correlated by performing the operation f(x, y)(-1)x+y prior to taking the transform of the image. If F and f are of same size M*N, where does the point (u, v) =(0,0) shifts?

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Q. Assuming that the origin of F(u, v), Fourier transformed function of f(x, y) an input image, has been correlated by performing the operation f(x, y)(-1)x+y prior to taking the transform of the image. If F and f are of same size M*N, then which of the following is an expression for H(u, v), the filter used for implementing Laplacian in frequency domain?

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Q. Computing the Fourier transform of the Laplacian result in spatial domain is equivalent to multiplying the F(u, v), Fourier transformed function of f(x, y) an input image, and H(u, v), the filter used for implementing Laplacian in frequency domain. This dual relationship is expressed as _________

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Q. An enhanced image can be obtained as: g(x,y)=f(x,y)-∇^2 f(x,y), where Laplacian is being subtracted from f(x, y) the input image. What does this conclude?

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Q. Why is scaling of Laplacian filtered images necessary?

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Q. Which of the following fact is true for the masks that includes diagonal neighbors than the masks that doesn’t?

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