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Another way to look at our threshold matrix is as a kind of probability matrix. Instead of offsetting the input pixel by the value given in the threshold matrix, we can instead use the value to sample from the cumulative probability of possible candidate colours, where each colour is assigned a probability or weight . Each colour’s weight represents it’s proportional contribution to the input colour. Colours with greater weight are then more likely to be picked for a given pixel and vice-versa, such that the local average for a given region should converge to that of the original input value. We can call this the N-candidate approach to palette dithering.
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I didn’t expect this classification task to be so simple—simple enough that a Hello World-level scikit-learn script, with minimal tweaks and some hard-coded rules, could produce a reasonably robust and accurate detector. Most of the effort was just waiting for LLMs to generate data…。业内人士推荐PDF资料作为进阶阅读
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