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Understanding Image Gradients

theailearner.com · 448 words · saved by 1 readers

In the previous blogs, we discussed different smoothing filters. Before moving forward, let’s first discuss Image Gradients which will be useful in edge detection, robust feature and texture matching. So, let’s first recall what a gradient is. In mathematics, the term gradient of a function means how a function is changing wrt. its arguments or independent variables. The gradient term is more frequently used for multi-variable functions. For a single variable function, we refer to this as the slope. The gradient of an N-variable function at each point is an N-D vector with the components given by the derivatives in the N-directions. e.g. for a 3-variable function (f(x,y,z)), the gradient, if it exists, is given by Thus, the gradient provides two pieces of information – magnitude and direction. The direction of the gradient tells us the direction of greatest increase while the magnitude represents the rate of increase in that direction. Because gradients are defined only for continuous

In the previous blogs , we discussed different smoothing filters. Before moving forward, let’s first discuss Image Gradients which will be useful in edge detection, robust feature and texture matching. So, let’s first recall what a gradient is. In mathematics, the term gradient of a function means how a function is changing wrt. its arguments or independent variables. The gradient term is more frequently used for multi-variable functions. For a single variable function, we refer to this as the slope. The gradient of an N-variable function at each point is an N-D vector with the com

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