![]() As we do the same with both image and character rasters, we will compare these weights instead of the original image.Īt first, we created custom matrices by hand to match the possible character shapes. When we convolve an image with a kernel, we will get a weight that shows how much the kernel modified the image's shape. So we will skip this part and will jump into the primary process. The character rasterization happens in the same way as the Intensity-based approach. The function can be accessed as ResourceObject. Since the implementation of the ImageASCII is already in the Wolfram Function Repository and the source code is also available, we will skip the implementation details and look into the concept of the solution. In our case, these functions were represented by the matrix's( f - image matrix and g - convolution kernel). In general, convolution is a mathematical operation on two functions ( f and g) that produces a third function ( f*g) expressing how one's shape is modified by the other. ![]() It turns out that convolution is the right choice for extracting that kind of feature that is why this approach is called the Convolution-based approach. That is why a new system is required to recover the underlying region. ![]() We used the mean intensity values to measure the distance, which ignored the underlying region structures. The previous post described an approach to perform ASCII art conversion.
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