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首页> 外文期刊>Pattern recognition and image analysis: advances in mathematical theory and applications in the USSR >A Fast Fourier based Feature Descriptor and a Cascade Nearest Neighbour Search with an Efficient Matching Pipeline for Mosaicing of Microscopy Images
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A Fast Fourier based Feature Descriptor and a Cascade Nearest Neighbour Search with an Efficient Matching Pipeline for Mosaicing of Microscopy Images

机译：基于快速的傅里叶的特征描述符和级联最近的邻居搜索，具有有效的匹配管道，用于显微镜图像的马赛明

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摘要

Automatic mosaicing is an important image processing application and we propose several improvements and simplifications to the image registration pipeline used in microscopy to automatically construct large images of whole specimen samples from a series of images. First of all we propose a feature descriptor based on the amplitude of a few elements of the Fourier transform, which makes it fast to compute and that can be used for any image matching and registration applications where scale and rotation invariance is not needed. Secondly, we propose a cascade matching approach that will reduce the time for the nearest neighbour search considerably, making it almost independent on feature vector length. Moreover, several improvements are proposed that will speed up the whole matching process. These are: faster interest point detection, a regular sampling strategy and a deterministic false positive removal procedure that finds the transformation. All steps of the improved pipeline are explained and the results comparative experiments are presented.
机译：自动拼接是一个重要的图像处理应用程序，我们提出了几种改进和简化对显微镜的图像登记流水线，以从一系列图像自动构建整个样本样本的大图像。首先，我们提出了一种基于傅里叶变换的几个元素的幅度的特征描述符，这使得计算得以快速计算，并且可以用于不需要比例和旋转不变性的任何图像匹配和注册应用程序。其次，我们提出了一种级联匹配方法，可以减少最近邻的时间的时间，使其几乎独立于特征向量长度。此外，提出了几种改进，以加速整个匹配过程。这些是：更快的兴趣点检测，定期采样策略和确定转换的确定性假剥离程序。解释改进的管道的所有步骤，并提出了结果对比实验。

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