SGEER: Exploratory Research on Analyzing Forward-Scan Sonar Video Imagery by Computer Vision Techniques

这个为期一年的SGER项目将探索从运动方法到声学视频图像的结构应用的数学模型。计算机声纳处理主要局限于经典的单帧方法，利用二维纹理或物体/阴影形状线索进行场景/目标检测、分割、分类和识别。很少，如果有的话，工作已经探索了潜在的使用多个视图的3-D目标重建，这是变得相当重要的高频率声学摄像机产生高分辨率的视频在低能见度条件下变得越来越可用。具体目标是探索声纳投影和运动模型的制定各种3-D形状和运动估计方法，推导基于模型的解决方案，确定一些有前途的方法，设计计算机视频分析和解释技术，更普遍地发展更好地了解声纳数据处理的复杂性。该研究项目的更广泛影响包括为实现自动水下测绘、无人水下航行器基于图像的定位和导航、航行器制导和控制、目标分类和识别等方面的关键能力奠定基础，最终能够更有效和高效地部署水下成像平台，用于各种不同的应用，包括海底科学研究和勘探。此外，技术发展将导致1）增强声纳视频技术，以实现分析结果预测的最佳性能，以及2）促进视觉和声纳社区之间的合作，采用更多样化的方法，将声纳视频处理建立为更成熟的学科。

This one-year SGER project will explore mathematical models for the application of structure from motion methods to acoustic video imagery. Computer sonar processing has been dominantly limited to classical single frame methods for scene/target detection, segmentation, classification and identification by exploiting 2-D texture or object/shadow shape cues. Little, if any, work has explored the potential use of multiple views for 3-D target reconstruction, which is becoming rather important as high-frequency acoustic cameras producing high-resolution video in low visibility conditions are becoming increasingly more available. The specific goals are to explore sonar projection and motion models in the formulation of various 3-D shape and motion estimation methods, derivation of model-based solutions, identification of some promising approaches for devising computer video analysis and interpretation techniques, and more generally developing a better understanding of the complexities in the processing of sonar data. The research project's broader impacts comprise establishing the foundation to realize critical capabilities in automated underwater mapping, image-based positioning and navigation ofunmanned underwater vehicles, vehicle guidance and control, target classification and recognition, etc., eventually enabling more effective and efficient day-today deployment of underwater imaging platforms for a range of diverse applications, including benthic scientific research and exploration. Furthermore, technical developments will lead to 1) enhancing the sonar video technology for optimum performance as predicted by analytical results, and 2) promoting collaboration between vision and sonar communities with more diversified approaches that are necessary to establish sonar video processing as a more mature discipline.