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Entropy thresholding and its parallel algorithm on the reconfigurable array of processors with wider bus networks

机译:具有更宽总线网络的可重配置处理器阵列上的熵阈值及其并行算法

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Thresholding is the most commonly used technique in image segmentation. We first propose an efficient sequential algorithm to improve the relative entropy-based thresholding technique. This algorithm combines the concepts of the relative entropy with that of the local entropy and also includes the quadtree hierarchical structure in it. Second, we derive a constant time parallel algorithm to solve this problem on the reconfigurable array of processors with wider bus networks (RAPWBN). The system bus bandwidth determines the capacity of data communication between processors. According to the results as shown by Li and Maresca (1989) and by Maresca and Li (1989), we know that the silicon area used by the switching control mechanism is far less than that used by the processor. Instead of increasing the number of processors, we extend the number of buses to increase the power of a parallel processing system. Such a strategy of utilizing the reconfigurable array of processors with wider bus networks not only has the advantage of saving silicon area but also increases the system power enormously. So, we use the RAPWBN to solve the entropy-based thresholding problem.
机译:阈值化是图像分割中最常用的技术。我们首先提出一种有效的顺序算法,以改进基于相对熵的阈值处理技术。该算法结合了相对熵和局部熵的概念,并在其中包括四叉树分层结构。其次,我们推导了恒定时间并行算法来解决具有较宽总线网络(RAPWBN)的可重配置处理器阵列上的此问题。系统总线带宽决定了处理器之间的数据通信能力。根据Li和Maresca(1989)以及Maresca和Li(1989)的结果,我们知道切换控制机制所使用的硅面积远小于处理器所使用的硅面积。我们没有增加处理器的数量,而是扩展了总线的数量,以增加并行处理系统的功能。这种利用具有较宽总线网络的可重配置处理器阵列的策略,不仅具有节省硅片面积的优点,而且还极大地增加了系统功率。因此,我们使用RAPWBN来解决基于熵的阈值问题。

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