A Novel Local Invariant Feature Extraction Method for High-dynamic Range Images

Yongjun Zhuang, Lei Liang
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引用次数: 1

Abstract

In order to perform target recognition under realistic conditions, it is necessary to extract local invariant features of a real scene. This paper proposes a novel local invariant feature extraction method for high dynamic range images. Firstly, based on the multi-exposure fusion HDR image calculation model, this paper introduces the perceptual model of human eye vision for scene irradiance, and establishes a complete theoretical model of HDR imaging and mapping. Then, this paper proposes a new LIFT extraction method, which performs LIFT detection on the scene reflection layer and LIFT description on the scene illumination layer. Finally, experiments have shown that this method can increase the number of correct matching of feature points of machine vision wide baseline images. The research results in this paper combine the invariant feature matching technology with the HDR image technology that can record the irradiance value of the real scene, thus expanding the application background of the image LIFT extraction technology.
一种新的高动态范围图像局部不变特征提取方法
为了在真实条件下进行目标识别,需要提取真实场景的局部不变特征。提出了一种新的高动态范围图像局部不变特征提取方法。首先,在多曝光融合HDR图像计算模型的基础上,引入人眼视觉对场景辐照度的感知模型,建立完整的HDR成像与映射理论模型。然后,本文提出了一种新的LIFT提取方法,对场景反射层进行LIFT检测,对场景照明层进行LIFT描述。最后,实验表明,该方法可以提高机器视觉宽基线图像特征点的正确匹配次数。本文的研究成果将不变特征匹配技术与能够记录真实场景辐照度值的HDR图像技术相结合,拓展了图像LIFT提取技术的应用背景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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