Preserving salience by maintaining perceptual differences for image creation and manipulation

计算机屏幕上或纸上的图像是我们日常生活的一大部分。 虽然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，美观和升华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通信能力。 在一个场景如何被感知和一个图像如何被感知之间有一个不稳定的映射。例如，尽管人类视觉系统不具有强烈的绝对色彩或强度感，但图像被存储为每个像素处的色彩或强度值的离散集合。 本研究的目标是通过更直接地将场景中的感知差异表达为图像中的显示差异来创建感知上显著的图像。 感知差异保留对于许多图像生成任务是有用的，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，以及用于识别X之间的对应和失配的隐藏特征发现技术，射线和可见光图像的历史上重要的绘画，通过分割和局部梯度统计。

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.