A Novel Algorithm for View and Illumination Invariant Image Matching
A Novel Algorithm for View and Illumination Invariant Image Matching
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DOI：
10.1109/tip.2011.2160271
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发表时间：
2012
影响因子：
10.6
通讯作者：
Yinan Yu;Kaiqi Huang;W. Chen;T. Tan
中科院分区：
文献类型：
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作者：
Yinan Yu;Kaiqi Huang;W. Chen;T. Tan
基于局部特征的图像匹配的挑战是视角和光照的变化。最近已经提出了许多方法来解决这些问题，通过使用不变的特征检测器和独特的描述符。然而，匹配性能仍然是不稳定和不准确的，特别是当视图或照明发生较大变化时。本文提出了一种视角和光照不变的图像匹配方法。我们迭代地估计图像的相对视图和照明的关系，将一个图像的视图转换为另一个，并将它们的照明归一化以进行精确匹配。我们的方法不旨在增加检测器的不变性，但提高匹配结果的准确性，稳定性和可靠性。在有效范围内，匹配性能不受视角和光照变化的影响。该方法在初始视角和照明方法失效时会失效，这为我们评价传统的检测器提供了一个新的视角。我们提出了两个新的检测器评估指标，即，有效角度和有效照明，这反映了最大允许的变化，在视图和照明，分别。大量的实验结果表明，我们的方法显着改善了传统的检测器，即使在大的变化，和两个指标是更有特色。
The challenges in local-feature-based image matching are variations of view and illumination. Many methods have been recently proposed to address these problems by using invariant feature detectors and distinctive descriptors. However, the matching performance is still unstable and inaccurate, particularly when large variation in view or illumination occurs. In this paper, we propose a view and illumination invariant image-matching method. We iteratively estimate the relationship of the relative view and illumination of the images, transform the view of one image to the other, and normalize their illumination for accurate matching. Our method does not aim to increase the invariance of the detector but to improve the accuracy, stability, and reliability of the matching results. The performance of matching is significantly improved and is not affected by the changes of view and illumination in a valid range. The proposed method would fail when the initial view and illumination method fails, which gives us a new sight to evaluate the traditional detectors. We propose two novel indicators for detector evaluation, namely, valid angle and valid illumination, which reflect the maximum allowable change in view and illumination, respectively. Extensive experimental results show that our method improves the traditional detector significantly, even in large variations, and the two indicators are much more distinctive.