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的重要组成部分。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、美观和精馏并没有以同样的速度提高。在我的研究中，我讨论了如何通过映射从一个域到另一个域的可见变化来创建或扩展图像的通信能力。在如何感知场景和如何感知图像之间存在一种不稳定的映射。例如，虽然人类的视觉系统没有强烈的绝对的色彩或强度感，但图像在每个像素上被存储为一组离散的颜色或强度值。本研究的目的是通过更直接地将场景的感知差异表达为图像的显示差异来创建感知显著的图像。感知差异保存对于许多图像生成任务都很有用，包括从3D模型自动创建技术插图，通过高维距离度量创建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识别历史重要画作的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.