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The Application of the 2D Structure Tensor in Visual Arts and Design

机译:2D结构张量在视觉艺术设计中的应用

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Tensors are a natural extension of matrices and vectors. They provide an ideal framework for abstracting relationships between related parameters. In image processing, tensors can be used to represent gradient information. The 2D structure tensor is one such representation, useful in corner detection and non-photorealistic rendering. This abstract summarizes my image processing filter based on the 2D structure tensor that generates artistic abstraction from photographs. With the 2D structure tensor, I explored ways to present the gradient directions of an image as artwork and investigated the relationship between the Gabor filter and the eigenvectors of the 2D structure tensor. Using this filter, I produce images that consist of a dense patchwork of lines, somewhat resembling certain artistic types of pen shading such as hatching and cross-contour shading. By applying this filter, I have found ways to categorize images that display salient groups of parallel lines. These images present a higher artistic quality and display better compositional style after being processed using the 2D structure tensor than the average photograph. Another finding is that not all resolutions of images are ideal for structure tensor processing and that an image size of 700-1000 pixels per side yields the best results from both a mathematical and artistic standpoint.
机译:张量是矩阵和载体的自然延伸。它们为相关参数之间的抽象关系提供了理想的框架。在图像处理中,张量可用于表示梯度信息。 2D结构张量是一种这样的表示,可用于拐角检测和非光容化渲染。该摘要总结了基于2D结构张量的图像处理过滤器,从照片中产生艺术抽象。利用2D结构张量,我探索了将图像的梯度方向呈现为艺术品,并研究了Gabor滤波器与2D结构张量的特征向量之间的关系。使用此过滤器,我产生由浓密的线条组成的图像,稍微类似于某些艺术类型的笔遮蔽,例如孵化和交叉轮廓阴影。通过应用此过滤器,我已经找到了分类的方法来归类显示平行线的突出的群体。这些图像呈现更高的艺术质量,并在使用2D结构张量之后显示比平均照片的2D结构张量在处理后显示更好的组成风格。另一个发现是,并非所有图像分辨率都是结构张量处理的理想选择,并且每侧700-1000像素的图像尺寸产生了来自数学和艺术的立场的最佳结果。

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