Parallel Algorithms For Intelligent Imaging And Vision At Low SNR

该项目的目标是开发算法，使智能成像和视觉系统能够在非常嘈杂的传感器数据下稳健而快速地运行。目前的方法在这种情况下表现很差，这是因为缺乏定义良好的最优化属性、底层离散表示的“脆性”以及大量的处理不一致和噪声伪影。改进的性能是通过低级信号处理和高级受数字和符号约束的目标识别任务的协同解决来实现的，而不是经典的顺序处理方法。总体方法是贝叶斯方法，并被表示为能量曲面的最小化。作为基于梯度的优化过程的副产品，所有图像处理操作都是自动产生的。这包括低水平变量和高水平变量的协同作用、模式约束的满足以及高水平建模信息的反馈。从体系结构的角度来看，提出的方法为并行计算提供了一种有价值的统一策略，并得到了适当的利用。这项工作预计将从根本上影响需要在有限的时间范围内与噪声传感器数据一起运行的应用程序。在DAP大规模并行处理机上计算了令人鼓舞的初步结果。

The objective of this project is to develop algorithms that will enable intelligent imaging add vision systems to operate robustly and rapidly with very noisy sensor data. Current methods perform poorly in this regime due to a lack of well-defined optimality properties, `brittleness' of the underlying discrete representations, and a deluge of processing inconsistencies and noise artifacts. The improved performance is achieved by a cooperative solution of low-level signal processing and high- level object recognition tasks subject to numeric and symbolic constraints, instead of the classical sequential processing method. The overall approach is Bayesian, and is formulated as the minimization of an energy surface. All the image-processing operations result automatically as by products of a gradient- based optimization process. This includes the synergistic interaction of low and high-level variables, satisfaction of pattern constraints, and feedback of high-level modelling information. From an architectural standpoint, the proposed method offers a valuable uniform strategy for parallel computation, which is duly exploited. This work is expected to fundamentally impact applications that are required to operate with noisy sensor data in a limited time frame. Encouraging preliminary results have been computed on a DAP massively- parallel processor.