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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Q. Edge pixels lie on darker or bright side of image can be determined by the

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Q. First derivatives in image segmentation produces

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Q. Edge localization id edge detection's

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Q. To avoid the negative values in lapacian image we use only

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Q. Masks for detection of specific lines are called

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Q. Line whose length and width is of one pixel is called

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Q. Detection of edge points is edge detection's

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Q. Intersection of two connected sets of an image should be

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Q. Isotropic detectors are independent of

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Q. For finding vertical lines we use mask of values

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Q. For diagonal edge detection we use 2D mask of

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

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Q. Gradient vector is also called

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Q. First and second derivatives can be computed using

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Q. First derivatives are positive at points on

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Q. For finding edge strength approach that is used is

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