![]() Also, the Intensity-based approach will have identical results with the Convolution-based one with only intensity kernel. The main difference with the Intensity-based approach is that if we have a single weight in the Intensity-based approach, we now have 65 of them. After we convolve an image and character raster, we compare the computed weights and determine which character raster can represent a particular part of the image. The result of the convolution is a matrix, where each element is a tensor of 65 weights. ![]() As a result, we have 65 kernels in total. VGG-16 neural network consists of 64 independent kernels we added an intensity kernel (kernel of all ones0 - basically to preserve the Intensity-based technique). However, experimentally, it turned out that known kernels from the VGG-16 neural network will lead to more accurate feature extraction. 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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