Q. To avoid the negative values taking absolute values in lapacian image doubles

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Q. First derivative approximation says that values of constant intensities must be

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Q. For finding lines at angle 45 we use mask of values

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Q. Second derivative approximation says that values along the ramp must be

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Q. Ri is a connected set, where is

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Q. Gradient magnitude images are more useful in

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Q. Image having gradient pixels is called

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Q. In laplacian images light shades of gray level is represented by

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Q. For noise reduction we use

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Q. Diagonal lines are angles at

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Q. Transition between objects and background shows

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Q. Horizontal lines are angles at

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Q. Standard deviation is referred to as noiseless if having the value

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Q. For edge detection we use

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Q. Step edge transition is between pixels over the distance of

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Q. Sobel gradient is not that good for detection of

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Q. Smoothness reduced the bricks of

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Q. Second derivative approximation says that it is nonzero only at

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Q. Method in which images are input and attributes are output is called

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Q. Computation of derivatives in segmentation is also called

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