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

机译：宽局部对比度图像的YAPE关键点检测算法的改进

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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，在许多现代关键点检测算法中，YAPE以其计算性能而著称，可在移动和嵌入式系统中使用。其缺点之一是对局部对比度的敏感性高，这导致高对比度区域中的检测密度较高，而低对比度区域中的检测缺失。在这项工作中，我们研究了YAPE的对比度敏感度，并提出了一种修正方案，以补偿具有较宽局部对比度范围的图像上的此属性（但另一个对比度不变点提取器YACIPE）。作为一个模型示例，我们考虑了交通标志识别问题，其中一些标志照明良好，而其他标志则处于阴影中，因此对比度较低。我们显示，与原始算法相比，对于拟议的修改，未检测到任何关键点的图像上的交通标志的数量要少40％。

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