Q. More smoothness is created by the mask of size

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Q. The direction of angle to the gradient is

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Q. Second derivative approximation says that value at end of ramp must be

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Q. In the sense of predicate two regions of the image must be

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Q. If all lines in the direction of defined direction of mask are wished to be found then we use

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Q. If inside the boundary is white otherwise black predicate is

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Q. One angle at which the lines in an image are not oriented is

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Q. Edge detection is based on

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Q. Dividing image into its objects is called

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Q. The coefficients of masks sum to

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Q. Sum of coefficients of derivative mask is

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Q. Fine details can be reduced by

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Q. The sum of LOG filter coefficients should be

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Q. Canny edge detection algorithm is based on

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Q. First derivatives are zero at points on

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Q. The resolution of the image (12x12) is

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Q. In laplacian images mid gray level is represented by

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Q. Locating the center of thick edges we use

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Q. Strong Heat signatures can be detected using

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Q. Second derivatives have stronger response to

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