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An embedded software-reconfigurable color segmentation architecture for image processing systems

机译:用于图像处理系统的嵌入式软件可重新配置的颜色分割架构

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Image segmentation is one of the first important and difficult steps of image analysis and computer vision and it is considered as one of the oldest problems in machine vision. Lately, several segmentation algorithms have been developed with features related to thresholding, edge location and region growing to offer an opportunity for the development of faster image/video analysis and recognition systems. In addition, fuzzy-based segmentation algorithms have essentially contributed to synthesis of regions for better representation of objects. These algorithms have minor differences in their performance and they all perform well. Thus, the selection of one algorithm vs. another will be based on subjective criteria, or, driven by the application itself. Here, a low-cost embedded reconfigurable architecture for the Fuzzy-like reasoning segmentation (FRS) method is presented. The FRS method has three stages (smoothing, edge detection and the actual segmentation). The initial smoothing operation is intended to remove noise. The smoother and edge detector algorithms are also included in this processing step. The segmentation algorithm uses edge information and the smoothed image to find segments present within the image, in this work the FRS segmentation algorithm was selected due to its proven good performance on a variety of applications (face detection, motion detection. Automatic Target Recognition (ATR)) and has been developed in a low-cost, reconfigurable computing platform, aiming at low cost applications. In particular, this paper presents the implementation of the smoothing, edge detection and color segmentation algorithms using Stretch S5000 processors and compares them with a software implementation using the Matlab. The new architecture is presented in detail in this work, together with results from standard benchmarks and comparisons to alternative technologies. This is the first such implementation that we know of, having at the same time high throughput, excellent performance (at least in standard benchmarks) and low cost.
机译:图像分割是图像分析和计算机视觉的第一个重要且困难的步骤之一,并且被认为是机器视觉中最古老的问题之一。最近,已经开发了几种具有与阈值,边缘位置和区域增长有关的特征的分割算法,从而为开发更快的图像/视频分析和识别系统提供了机会。此外,基于模糊的分割算法实质上有助于区域的合成,以更好地表示对象。这些算法在性能上有细微的差别,并且都表现良好。因此,一种算法与另一种算法的选择将基于主观标准,或者由应用程序本身驱动。在此,提出了一种用于模糊类推理分段(FRS)方法的低成本嵌入式可重构体系结构。 FRS方法包括三个阶段(平滑,边缘检测和实际分割)。初始平滑操作旨在消除噪声。此处理步骤中还包括平滑器和边缘检测器算法。分割算法使用边缘信息和平滑的图像来查找图像中存在的片段,在这项工作中,选择FRS分割算法是因为它在各种应用(面部检测,运动检测。自动目标识别(ATR) )),并且已针对低成本应用开发了低成本,可重配置的计算平台。特别是,本文介绍了使用Stretch S5000处理器的平滑,边缘检测和颜色分割算法的实现,并将它们与使用Matlab的软件实现进行了比较。在这项工作中将详细介绍新的体系结构,以及标准基准测试的结果以及与替代技术的比较。这是我们所知道的第一个这样的实现,同时具有高吞吐量,出色的性能(至少在标准基准测试中)和低成本。

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