Preserving salience by maintaining perceptual differences for image creation and manipulation

电脑屏幕上或纸上的图像是我们日常生活中的一大部分。尽管我们可以访问的图像数量呈指数级增长，但我们看到的信息的效率、美观和提炼并没有以同样的速度提高。在我的研究中，我解决了如何通过将可见的变化从一个域映射到另一个域来创建或扩展图像的传播能力。在如何感知场景和如何感知图像之间存在着不稳定的映射。例如，尽管人类视觉系统对颜色或强度没有很强的绝对感觉，但图像在每个像素上存储为一组离散的颜色或强度值。这项研究的目的是通过更直接地将场景中的感知差异表达为图像中显示的差异来创建感知显著的图像。感知差异保存对于可能的图像生成任务很有用，包括从3D模型自动创建技术插图，创建通过高维距离度量保留所有可见颜色变化的颜色到灰度转换算法，以及通过分割和局部梯度统计来识别历史上重要绘画的X射线和可见光图像之间的对应和不匹配的隐藏特征查找技术。

Images on computer screens or on paper are a large part of our everyday lives. Although the number of images we have access to is growing exponentially, the efficiency, beauty, and distillation of the information we see is not improving at the same rate. In my research, I address how to create or extend the communication power of images by mapping visible changes from one domain to another. There is a precarious mapping between how a scene would be perceived and how an image is perceived. For example, although the human visual system does not have a strong absolute sense of color or intensity, images are stored as a discrete set of color or intensity values at each pixel. The goal of this research is to create perceptually salient images by more directly expressing perceived differences in scenes as displayed differences in images. Perceptual difference preservation is useful for may image generation tasks including automatically creating technical illustrations from 3D models, creating a color to grayscale conversion algorithm that preserves all visible color changes through high-dimensional distance metrics, and for hidden feature finding techniques that identify correspondences and mismatches between X-ray and visible light images of historically significant paintings through segmentation and local gradient statistics.