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Image matching using peak signal-to-noise ratio-based occlusion detection

机译：使用基于峰值信噪比的遮挡检测进行图像匹配

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摘要

A new identification mechanism is introduced for the purpose of locating objects partially occluded under low peak signal-to-noise ratio (PSNR) environment in two-dimensional grey scale image.The proposed occlusion detection is based on the utilisation of the fact that the higher the PSNR, the less the impairment of the image. Most existing methods require a training process before recognition tasks or fail to obtain good results when objects are partially occluded under low PSNR environment. The new identification mechanism uses a self-adaptively adjusted threshold for providing a more exact occlusion detection and a correlation-coefficient evaluation process for reducing false positives, allowing for better accuracy in classifying and locating partially occluded objects under low PSNR. The performance of the proposed framework is confirmed through 100 occluded images with various noise levels. The experimental results show that the new proposed algorithm is not only more robust against image noise and partial occlusion, but also provides a significantly improved object localisation/recognition performance when compared with normalised cross-correlation and selective correlation coefficient.
机译：为了识别二维灰度图像中低峰值信噪比（PSNR）环境下部分被遮挡的物体，引入了一种新的识别机制。 PSNR越小，图像的损伤越小。大多数现有方法都需要在识别任务之前进行训练，否则当在低PSNR环境下部分遮挡对象时将无法获得良好的结果。新的识别机制使用自适应调整的阈值来提供更精确的遮挡检测，并使用相关系数评估过程来减少误报，从而在低PSNR下对部分遮挡的物体进行分类和定位时具有更高的准确性。拟议框架的性能通过具有各种噪声水平的100张遮挡图像得到确认。实验结果表明，与归一化互相关和选择性相关系数相比，新算法不仅对图像噪声和部分遮挡具有更强的鲁棒性，而且还提供了显着改善的对象定位/识别性能。

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  • 来源
    《Image Processing, IET》 |2012年第5期|p.483-495|共13页
  • 作者

    Yoo J.-C.; Ahn C.W.;

  • 作者单位

    Sungkyunkwan University, Republic of Korea;

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  • 正文语种 eng
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