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Fast Contour-Tracing Algorithm Based on a Pixel-Following Method for Image Sensors

机译:基于像素跟踪的图像传感器快速轮廓跟踪算法

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摘要

Contour pixels distinguish objects from the background. Tracing and extracting contour pixels are widely used for smart/wearable image sensor devices, because these are simple and useful for detecting objects. In this paper, we present a novel contour-tracing algorithm for fast and accurate contour following. The proposed algorithm classifies the type of contour pixel, based on its local pattern. Then, it traces the next contour using the previous pixel’s type. Therefore, it can classify the type of contour pixels as a straight line, inner corner, outer corner and inner-outer corner, and it can extract pixels of a specific contour type. Moreover, it can trace contour pixels rapidly because it can determine the local minimal path using the contour case. In addition, the proposed algorithm is capable of the compressing data of contour pixels using the representative points and inner-outer corner points, and it can accurately restore the contour image from the data. To compare the performance of the proposed algorithm to that of conventional techniques, we measure their processing time and accuracy. In the experimental results, the proposed algorithm shows better performance compared to the others. Furthermore, it can provide the compressed data of contour pixels and restore them accurately, including the inner-outer corner, which cannot be restored using conventional algorithms.
机译:轮廓像素将对象与背景区分开。跟踪和提取轮廓像素被广泛用于智能/可穿戴图像传感器设备,因为它们对于检测物体非常简单且有用。在本文中,我们提出了一种新颖的轮廓跟踪算法,用于快速准确的轮廓跟踪。所提出的算法基于轮廓像素的局部模式对轮廓像素的类型进行分类。然后,它使用前一个像素的类型来跟踪下一个轮廓。因此,可以将轮廓像素的类型分类为直线,内角,外角和内外角,并且可以提取特定轮廓类型的像素。此外,它可以快速跟踪轮廓像素,因为它可以使用轮廓情况确定局部最小路径。另外,该算法能够利用代表点和内外角点压缩轮廓像素的数据,并且能够从数据中准确地还原轮廓图像。为了将提出的算法与传统技术的性能进行比较,我们测量了它们的处理时间和准确性。在实验结果中,与其他算法相比,该算法具有更好的性能。此外,它可以提供轮廓像素的压缩数据并准确地恢复它们,包括内外角,这是使用常规算法无法恢复的。

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