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An alternative method for designing parallel image recognition systems: a feasibility study

机译:设计并行图像识别系统的另一种方法:可行性研究

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The most successful image recognition systems have been developed in software. This method has proved to be quite laborious and the execution time is intensive. An alternative method of designing image recognition systems is needed. The goal is to design a reliable, compact, high speed, low cost recognition system. One way to reach this goal is to design the image recognition system via a massively parallel hardware architecture. The present study researches the feasibility of developing a generalized procedure to design unique image recognition systems. These unique image recognition systems will incorporate special purpose processors that will work at a speed that is several orders of magnitude faster than software image processing systems. These systems have great application perspectives, especially in the robotics and industrial fields. One of the most important aspects of these systems-is the unique compact size as compared with other systems of comparable speed. Also, these systems are independent modules that will output, in most cases, a signal to a controller without the need to use a conventional computer or processor. The procedure to be developed provides a new way of designing an image processing system by concentrating on modularity and massive parallelism. Combining the modules will provide a special purpose processing system that achieves object detection and recognition at a high speed not even possible on a general purpose supercomputer. The other advantages include the low cost of implementation and the compact size of the whole system.
机译:最成功的图像识别系统已通过软件开发。事实证明,这种方法非常费力并且执行时间很长。需要一种设计图像识别系统的替代方法。目的是设计一个可靠,紧凑,高速,低成本的识别系统。实现此目标的一种方法是通过大规模并行硬件体系结构设计图像识别系统。本研究研究开发通用程序以设计独特的图像识别系统的可行性。这些独特的图像识别系统将结合专用处理器,其工作速度将比软件图像处理系统快几个数量级。这些系统具有广阔的应用前景,尤其是在机器人技术和工业领域。这些系统最重要的方面之一是与其他速度相当的系统相比,其独特的紧凑尺寸。而且,这些系统是独立的模块,在大多数情况下,这些信号将输出信号到控制器,而无需使用常规的计算机或处理器。通过集中于模块化和大规模并行性,将要开发的程序提供了一种设计图像处理系统的新方法。组合这些模块将提供一种特殊目的的处理系统,该系统可以实现高速的对象检测和识别,而这在通用超级计算机上甚至是不可能的。其他优点包括实施成本低和整个系统紧凑。

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