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Image are often viewed as matricies of 2D,3D, or nD. For example, a grayscale image can be represented by a 2D matrix where the values represent the intensity (grayscale value) of each pixel. For a color image, 3 2D matricies of the same size can represent the intesity of each channel, such as RGB or HSV. Signal filtering operations are often performed on matrix representations of images. For example, convultion and edge detection. http://www.gergltd.com/cse486/project2/ Edge detection lab.

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  • Matrix Approach
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  • Image are often viewed as matricies of 2D,3D, or nD. For example, a grayscale image can be represented by a 2D matrix where the values represent the intensity (grayscale value) of each pixel. For a color image, 3 2D matricies of the same size can represent the intesity of each channel, such as RGB or HSV. Signal filtering operations are often performed on matrix representations of images. For example, convultion and edge detection. <a href="http://www.gergltd.com/cse486/project2/">http://www.gergltd.com/cse486/project2/</a> Edge detection lab.
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abstract
  • Image are often viewed as matricies of 2D,3D, or nD. For example, a grayscale image can be represented by a 2D matrix where the values represent the intensity (grayscale value) of each pixel. For a color image, 3 2D matricies of the same size can represent the intesity of each channel, such as RGB or HSV. Signal filtering operations are often performed on matrix representations of images. For example, convultion and edge detection. <a href="http://www.gergltd.com/cse486/project2/">http://www.gergltd.com/cse486/project2/</a> Edge detection lab.
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