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ROBUST STEREO MATCHING COMBINING SIFT DESCRIPTOR WITH NCC UNDER MRF FRAMEWORK

机译:在MRF框架下将SIFT描述符组合SIFT描述符的强大立体声匹配

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There have been several stereo matching methods that perform well under the circumstance of color consistency. However, various factors including radiometric and device variations between images will drop color consistency and then seriously degrade the performance of those methods. In this paper, we propose a robust method to cover these situations. We use a measurement combining SIFT descriptor in intensity space after color histogram equalization with Normalized Cross Correlation (NCC) in color invariant log-chromaticity intensity space to compute the cost of point correspondence. The measurement ensures that illumination-independent gradient information and color invariant correlated information are integrated properly. We evaluate our method and find it outperform state-of-the-art algorithms, in particular on the datasets for radiometric variations.
机译:已经有几种立体声匹配方法,在颜色一致性的情况下表现良好。然而,各种因素包括图像之间的辐射和设备变化会降低颜色一致性,然后严重降低这些方法的性能。在本文中,我们提出了一种稳健的方法来涵盖这些情况。在颜色不变日志色度强度空间中,在颜色直方图均匀(NCC)中使用强度空间中的强度空间中的测量SIFT描述符与颜色不变日志色度强度空间中的强度空间中的测量值,以计算点对应的成本。测量确保了与照明无关的梯度信息和颜色不变相关信息正常集成。我们评估我们的方法,并发现它优于最先进的算法,特别是在数据集上进行辐射变化。

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