Compute Image Gradient Intensity - No Noise

• MHB
• robi10101298
In summary, to compute the magnitude of edges or the gradient of the contour for an image with 11x11 pixels and a 5x5 pixel square in the center, you can use the compass operator after converting the image to grayscale and applying a Gaussian filter to reduce noise. The gradient can be computed using a simple derivation method due to the lack of noise in the image.
robi10101298
Hello guys, if I have an image with 11x11 pixels and in the center of the image is a square of 5x5 pixels, with the gray level of the background 0 and the gray level of the square is 50. How can I compute the result of the magnitude of edges(intensity of the contour) or better said the gradient of the contour given by the compass operator for this image taking into account that the image is not noisy? I need to apply simple derivation because the image it's not noisy.

Hi there,

To compute the magnitude of edges or the gradient of the contour for this image using the compass operator, you can use the following steps:

1. First, convert the image to grayscale if it is not already in grayscale.

2. Next, apply a Gaussian filter to smooth out any noise in the image. This will help to reduce any false edges that may be caused by noise.

3. Then, use the compass operator on the smoothed image to detect the edges. The compass operator is a type of edge detection algorithm that uses a set of predefined kernels to detect edges in multiple directions.

4. Once you have the edge detection results, you can compute the magnitude of the edges by taking the square root of the sum of squares of the horizontal and vertical edge values at each pixel.

5. Finally, you can visualize the gradient of the contour by plotting the magnitude values on a gradient map, where the higher values correspond to stronger edges.

Since the image is not noisy, you can use a simple derivation method to compute the gradient of the contour. However, if the image was noisy, you may need to use a more robust method such as the Canny edge detector.

I hope this helps! Let me know if you have any further questions.

1. What is image gradient intensity?

Image gradient intensity is a measure of the change in pixel intensity between neighboring pixels in an image. It indicates the rate at which the intensity of an image changes in a specific direction.

2. How is image gradient intensity calculated?

Image gradient intensity is calculated by taking the derivative of the image in the x and y directions. This can be done using various mathematical methods, such as the Sobel, Prewitt, or Roberts operators.

3. What is the purpose of computing image gradient intensity?

The purpose of computing image gradient intensity is to identify and highlight edges and boundaries in an image. This can be useful in various image processing tasks, such as feature detection, image segmentation, and object recognition.

4. What is the difference between image gradient intensity and image gradient magnitude?

Image gradient magnitude is the overall strength of the image gradient, while image gradient intensity is the rate of change in intensity in a specific direction. In other words, gradient magnitude measures the amount of change in an image, while gradient intensity measures the rate of change.

5. How does noise affect image gradient intensity?

Noise in an image can affect the accuracy of image gradient intensity calculations. It can introduce false edges and distort the true gradient intensity values. Therefore, it is important to pre-process an image to reduce noise before computing its gradient intensity.

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