How does the Sobel operator approximate image gradients for edge detection?

WHAT THIS TESTS: The interviewer wants to see if you can decompose the Sobel operator into its mathematical building blocks: separable convolution, central finite differences, and vector magnitude. It is not enough to name the kernels; you must explain why they are shaped the way they are and how they approximate the image gradient.

A GOOD ANSWER COVERS: First, state that Sobel estimates the gradient of the image intensity function using two 3x3 kernels, Gx and Gy. Second, show that each kernel is separable into a one-dimensional derivative filter and a one-dimensional smoothing filter. For Gx, the derivative row is [-1, 0, 1] and the smoothing column is [1, 2, 1] transposed; Gy swaps the roles. Third, explain that the derivative term approximates the partial derivative via central differencing, while the smoothing term reduces noise sensitivity by averaging orthogonal to the derivative direction. Fourth, describe the combination step: at each pixel you compute the gradient vector [Gx, Gy], then take its magnitude as the square root of Gx squared plus Gy squared, or sometimes the L1 norm absolute Gx plus absolute Gy for computational speed.

COMMON WRONG ANSWERS: A major red flag is presenting the kernels as arbitrary magic numbers without linking them to differentiation and averaging. Another mistake is claiming Sobel is rotationally invariant or isotropic; the Wikipedia reference explicitly notes the operator is called isotropic but the approximation is crude and not truly rotation invariant. Candidates also err by describing the kernels as pure Gaussian smoothing or pure derivative filters, missing the product structure entirely. Finally, forgetting to mention magnitude combination or confusing the direction angle arctan2(Gy, Gx) with the edge magnitude is a signal of shallow understanding.

LIKELY FOLLOW-UPS: An interviewer might ask how Sobel compares to the Prewitt or Roberts operators, which use simpler uniform smoothing or smaller 2x2 stencils and are more noise sensitive. They might ask why the smoothing weights are [1, 2, 1] rather than [1, 1, 1], which tests whether you know the triangular kernel approximates a Gaussian and gives more weight to the center pixel. Another follow-up is how to make the operator scale invariant or how to handle edges at different scales, leading naturally to the Canny edge detector which uses Sobel as a component but adds non-maximum suppression and hysteresis thresholding.

ONE CONCRETE EXAMPLE: Imagine a single vertical step edge in a 7x7 image where the left half is intensity 50 and the right half is intensity 150. Convolving the central row with the Gx kernel [-1, 0, 1; -2, 0, 2; -1, 0, 1] at the edge pixel yields a large positive response because the right side is brighter. Convolving the same location with Gy produces approximately zero because intensities are constant vertically. The edge magnitude is therefore dominated by Gx, giving a strong edge response of roughly 400 in the center, while flat regions produce near zero magnitude.

Source: Wikipedia: Sobel operator