Developing a quantitative model of human preattentive vision
Developing a quantitative model of human preattentive vision
复制标题
开发人类前注意视觉的定量模型
DOI：
10.1109/21.44061
复制
发表时间：
1989
期刊：
影响因子：
--
通讯作者：
C. T. Ng
中科院分区：
文献类型：
--
作者：
R. Conners;C. T. Ng
为了开发强大的计算机视觉系统方法，具有一系列适用性的方法，需要能够匹配人类感知性能水平的早期视觉操作员。这意味着需要开发能够以统一和一致的方式执行各种图像分析任务的操作员。这些图像分析任务包括找到均匀但不同灰度级和纹理的区域之间的边界。它们还包括测量完形分组概念，如一致性和接近性，以便将这些概念纳入分割过程。最后，操作员应该能够测量信息，使表面的特性变得明确，例如，即所谓的阴影恢复形状和纹理恢复形状方法。开发强大的方法来执行这些任务对应于开发人类前注意力视觉的定量模型。提出了这样一个定量模型的基础，这与计算机视觉中的当前理论相反，但似乎为图像分析任务提供了一种统一的方法，并解释了一些感知现象。>
To develop robust computer vision system methodologies, ones that have a range of applicability, early vision operators capable of matching a level of human perceptual performance are required. This implies the need to develop operators that can perform a variety of image analysis tasks in a unified and consistent fashion. These image analysis tasks include finding boundaries between regions of uniform but different gray levels and textures. They also include gauging Gestalt grouping concepts such as uniformity and proximity, so that these concepts are incorporated into the segmentation process. Lastly, the operators should be able to gauge information that allows the characteristics of surfaces to be made explicit, e.g., so-called shape-from-shading and shape-from-texture methods. Developing robust methods for performing these tasks corresponds to developing a quantitative model of human preattentive vision. A basis for such a quantitative model is proposed that is contrary to current theories in computer vision but that seemingly provides a unified method for image analysis tasks and to explain a number of perceptual phenomena. >