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8 Colour Quantization of Colour Construct Code in CIELAB Colour Space Using K-Means Clustering and Hungarian Assignment

机译:8使用K-Means Clustering和Hungarian Assistment的Cielab颜色空间中颜色构造码的颜色结构

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Color Construct Code (CCC) has been introduced to store data with larger size by applying colour feature as compared to traditional 2D QR code. To decode CCC, colour quantization and colour correction process in pre-decoding process are essentially important in order to obtain data. Unfortunately, colour feature in CCC is always easily influenced by illumination. Meanwhile, colour space i.e. RGB and HSV is also device-dependent. Our work addresses these two issues and introduces a method to quantize and correct the 8 colour of CCC based on its CIELAB colour space. We combined K-mean clustering for the colour reduction and Hungarian assignment for the colour correction purpose. The results showed that our method does not only manage to correct the colour under varying illumination, but it is also independent to the device.
机译:彩色构造代码(CCC)已被引入以通过应用颜色特征与传统的2D QR码相比将具有较大尺寸的数据存储数据。为了解码CCC,在预解码过程中的颜色量化和颜色校正过程基本上是重要的,以获得数据。不幸的是,CCC中的颜色特征始终容易受到照明的影响。同时,颜色空间即RGB和HSV也依赖于设备。我们的工作解决了这两个问题,并介绍了一种方法来量化和校正CCC的8种颜色的CIELAB颜色空间。我们组合K-Mean Clustering用于颜色校正目的的颜色减少和匈牙利分配。结果表明,我们的方法不仅可以纠正各种照明下的颜色,而且它也独立于设备。

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