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In computer vision, the Sobel operator is a simple edge detection algorithm using the 1st derivative of the intensity information. The operator uses two 3x3 kernels convolved with the original image to produce a map of intensity gradient. The areas of highest gradient are where the intensity of the image changes rapidly over a few pixels, and are thus likely to represent edges. Two convolution kernels are needed to detect the first-order derivative of both horizontal and vertical changes in a 2-dimensional image. If we define as the source image, we can compute: Where

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  • Sobel
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  • In computer vision, the Sobel operator is a simple edge detection algorithm using the 1st derivative of the intensity information. The operator uses two 3x3 kernels convolved with the original image to produce a map of intensity gradient. The areas of highest gradient are where the intensity of the image changes rapidly over a few pixels, and are thus likely to represent edges. Two convolution kernels are needed to detect the first-order derivative of both horizontal and vertical changes in a 2-dimensional image. If we define as the source image, we can compute: Where
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abstract
  • In computer vision, the Sobel operator is a simple edge detection algorithm using the 1st derivative of the intensity information. The operator uses two 3x3 kernels convolved with the original image to produce a map of intensity gradient. The areas of highest gradient are where the intensity of the image changes rapidly over a few pixels, and are thus likely to represent edges. Two convolution kernels are needed to detect the first-order derivative of both horizontal and vertical changes in a 2-dimensional image. If we define as the source image, we can compute: Which can then be combined to give the overall magnitudes using: Using this information, we can also calculate the gradient's direction: Where will be 0 for a vertical edge, and will increase for edges anti-clockwise of this.
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