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稳健运行 并且快速地处理非常嘈杂的传感器数据。 目前的方法执行 由于缺乏定义明确的最优性， 特性，潜在离散的“脆性” 陈述，以及大量的处理不一致， 噪声伪影。 改进的性能是通过 低层信号处理和高层信号处理的协同解决方案， 水平对象识别任务受数字和符号 约束，而不是经典的顺序处理 法 总体方法是贝叶斯的，并且公式化为 能量表面的最小化。 所有的图像处理 操作结果自动作为梯度的副产品- 基于优化过程。 这包括协同 低水平和高水平变量的相互作用， 模式约束和高级建模的反馈 信息. 从建筑学的角度来看， 方法提供了一个有价值的统一策略，并行 计算，这是适当利用。 这项工作预计将 从根本上影响到需要运行的应用程序 在有限的时间范围内处理有噪声的传感器数据。 鼓励 初步结果已经在DAP上进行了大量计算- 并行处理机

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.