Hello. I have recently been spending a lot of time dithering. In image processing, dithering is the act of applying intentional perturbations or noise in order to compensate for the information lost during colour reduction, also known as quantisation. In particular, I’ve become very interested in a specific type of dithering and its application to colour palettes of arbitrary or irregular colour distributions. Before going into detail however, let’s briefly review the basics of image dithering.
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Knowing this, we can modify the N-Convex algorithm covered earlier such that the candidate weights are given by the barycentric coordinates of the input pixel after being projected onto a triangle whose vertices are given by three surrounding colours, abandoning the IDW method altogether1. This results in a fast and exact minimisation of , with the final dither being closer in quality to that of Knoll’s Algorithm.
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智能体发展的另一个关键方向，是成为人类的“个人助手”或“智能代理”。与其他类型的智能体相比，这一方向的门槛更高，因为它需要更强的个性化能力、长期记忆、跨场景泛化能力以及更严格的安全边界。个人助手型智能体有潜力重塑人类与世界的交互方式——无论是购物、社交，还是信息获取——并可能对很多产业的商业逻辑产生颠覆性影响。
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