Preattentive Vision Based Automatic Recognition of Smoke and Obscurant Clouds

[9361907]野外跟踪森林火灾或火山产生的烟雾和隐蔽云需要确定从云周围不同角度扫描的可见图像中显示的二维云的范围。目前，二维云的范围是由操作员手动定位云边界上的点来确定的，这一过程既昂贵又耗时，而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，开发基于人类预注意视觉过程的计算机视觉算法，用于自动识别烟云。本研究的目标包括研究人类预注意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以检测云区域，利用这些算法开发神经网络模式识别系统，并将该系统与用户友好的图形界面集成。基于人类预关注视觉过程的图像分析特征的创新使用对于自动云检测具有重要意义。由于以人类视觉预注意为模型的特征能够以独立于场景的方式发挥作用，因此将演示一种优于现有方法的自动识别模糊云的系统。这是SBIR一期项目。＊＊＊

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. ***