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Modification of YAPE Keypoint Detection Algorithm for Wide Local Contrast Range Images

机译：宽局部对比度范围图像的yape keypoint检测算法的修改

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摘要

Keypoint detection is an important tool of image analysis, and among many contemporary keypoint detection algorithms YAPE is known for its computational performance, allowing its use in mobile and embedded systems. One of its shortcomings is high sensitivity to local contrast which leads to high detection density in high-contrast areas while missing detections in low-contrast ones. In this work we study the contrast sensitivity of YAPE and propose a modification which compensates for this property on images with wide local contrast range (Yet Another Contrast-Invariant Point Extractor, YACIPE). As a model example, we considered the traffic sign recognition problem, where some signs are well-lighted, whereas others are in shadows and thus have low contrast. We show that the number of traffic signs on the image of which has not been detected any keypoints is 40% less for the proposed modification compared to the original algorithm.
机译：关键点检测是图像分析的重要工具，并且在许多当代关键点检测算法中，yape是其计算性能的，允许其在移动和嵌入式系统中使用。其缺点之一对局部对比度具有很高的敏感性，这导致高对比度区域的高检测密度，同时缺少低对比度的检测。在这项工作中，我们研究了yape的对比度灵敏度，并提出了一种修改，该修改在具有宽局部对比度范围（又一个对比的积分点，酵i缘）的图像上补偿了这种特性。作为一个模型示例，我们考虑了交通标志识别问题，其中一些迹象是良好的照明，而其他迹象则在阴影中，因此具有低对比度。对于所提出的修改与原始算法相比，我们尚未检测到其图像上尚未检测到的图像上的交通标志数为40％。

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