Preattentive Vision Based Automatic Recognition of Smoke and Obscurant Clouds

来自森林火灾或火山的烟雾和遮蔽云的现场跟踪需要确定从云的各个角度扫描的可见光图像中所示的二维云范围。 目前，二维云的范围是由一个操作员确定的，他手动定位云周边上的点-这一过程被发现是昂贵的、耗时的并且容易受到人为误判的影响。 为了解决这个问题，本项目进行了一项研究，为发展的计算机视觉算法的基础上，人类前注意的视觉过程的烟雾云的自动识别。 本研究的目标包括研究人类前注意力的视觉，以确定重要的视觉线索，将这些视觉线索纳入计算机视觉算法检测云区域，开发一个神经网络模式识别系统，利用这些算法，并整合系统与用户友好的图形界面。 基于人类前注意视觉过程的图像分析特征的创新使用对于自动云检测是重要的。 由于模仿人类视觉前注意的功能能够在场景无关的方式，用于自动识别模糊云，这是上级目前的方法的系统将被证明。 这是SBIR第一阶段项目。 ***

9361907 Ng Field tracking of smoke and obscurant clouds from forest fires or volcanoes requires determination of the two- dimensional cloud extent shown in visible images scanned from various angles about the clouds. At the present time, two-dimensional cloud extent is determined by an operator who manually locates points on the cloud perimeter -- a procedure which has been found to be expensive, time consuming and vulnerable to human misjudgment. To address this problem, this project undertakes a study for the development of computer vision algorithms based on human preattentive visual processes for the automatic recognition of smoke clouds. The objectives of this research include a study of human preattentive vision to determine important visual cues, incorporation of these visual cues into computer vision algorithms for detecting cloud regions, development of a neural network pattern recognition system utilizing these algorithms, and integration of the system with a user-friendly graphical interface. The innovative use of image analysis features based on human preattentive visual processes for automated cloud detection is significant. Since features modeled on human visual preattention are capable of functioning in a scene- independent manner, a system for automatically recognizing obscurant clouds that is superior to present methods will be demonstrated. This is an SBIR Phase I project. ***