Real-Time Signal Processing using Hardware Implementation of Bio-Inspired Systems

预计视觉促进将在汽车安全、手持设备和无线成像中发挥重要作用。同时，对成像系统减小功率和面积的需求正在增加。因此，需要新的解决方案来实现这一目标。传统的机器视觉系统必须以高速对视场进行采样。它还必须为每帧存储大量无用的信息，数据量在兆字节范围内。有用的信息被埋在每个帧的原始数据下，反过来，必须进行大量的处理来提取it.For光流测量用于运动检测的图像传感器中，处理任务甚至更加复杂，因为光流是由空间以及时间现象。这意味着用于光流测量的视觉系统必须被设计为测量时空信息，这需要大量的计算任务。与传统的视觉系统相比，专用的视觉芯片是一个集成电路，包含图像采集和处理。在这个建议中，先进的集成视觉传感器与并发像素阵列，使用模拟和混合信号结构的芯片将被调查。集成的传感器捕获并预处理图像，这意味着它只会从每个像素中提取有用的数据。电路和算法的灵感来自昆虫视觉的生物模型。在昆虫中，眼睛元素具有类似于集成像素的2-D拓扑结构。然而，昆虫的神经回路能够适应可变的光线，并且对于检测移动物体非常有用。集成视觉传感器可以通过模拟电路实现，这反过来又可以在低对比度环境中有效地工作。最重要的是，这种方法允许电路在小封装中实现，可以集成在复杂的系统中，作为智能传感器网络的预处理单元。

It is expected that vision facilitation will play an important part in automotive safety, handheld devices, and wireless imaging. At the same time demands on imaging systems to reduce power and area are increasing. Consequently, novel solutions are required to achieve this goal. A conventional machine vision system has to sample a visual field at high speed. It also has to store large amounts of useless information, in the range of megabytes of data, for each frame. Useful information is buried under the raw data in each frame which, in turn, has to be heavily processed to extract it. For optical flow measurement in image sensors used for motion detection, the processing task is even more complicated since optic flow is comprised of both spatial as well as temporal phenomena. This means that the vision system for optical flow measurement has to be designed to measure the spatio-temporal information, which requires a high number of computational tasks. In contrast to conventional vision systems, a dedicated vision chip is an integrated circuit that contains both image acquisition and processing. In this proposal, advanced integrated vision sensors with concurrent pixel arrays, using analog and mixed-signal structures chip will be investigated. The integrated sensor captures and pre-processes the image, which means it will extract only useful data from each pixel. The circuits and algorithms are inspired by biological models of insect vision. In insects, the eye element has a 2-D topology that is similar to integrated pixels. However, the neural circuits in insects adapt to variable light and are useful for detecting moving objects. The integrated vision sensor can be implemented by analog circuits, which in turn can effectively function in low contrast environments. Most importantly, this method allows circuitry to be implemented in a small package, which can be integrated in complex systems, serving as a pre-processing unit for intelligent sensor networks.