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The Retinal Image Mosaic Based on Invariant Feature and Hierachical Transformation Models

机译:基于不变特征和层级变换模型的视网膜图像马赛克

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It is important to determine the stable keypoints and select transformation models for image registration and mosaic. In this paper a method is presented for retinal image mosaic. Central to the new method is to detect the PCA-SIFT (Principal Components Analysis-Scale Invariant Feature Transform) feature and estimate the quadratic transformation model which is employed to simulate the anatomy of human eyes. The transformations models are estimated by matching PCA-SIFT landmarks. The hierachical notion is used to map the Inter-Image. The random sample consensus (RANSAC) is used to estimate the affine transformation model and remove exterior point. The quadratic is estimated by M-estimator. And the weighted mean is used to Stitch retinal images. The proposed approach can effectively realize the retinal image mosaic.
机译:确定稳定的关键点并选择变换模型,用于图像配准和马赛克。在本文中,提出了一种用于视网膜图像马赛克的方法。新方法的核心是检测PCA-SIFT(主成分分析级不变特征变换)特征,并估计用于模拟人眼解剖结构的二次变换模型。通过匹配PCA-SIFT标志性估算转换模型。 Hierachical概念用于映射图像。随机样本共识(RANSAC)用于估计仿射变换模型并移除外部点。二次估计由M估计估计。并且加权均值用于缝合视网膜图像。所提出的方法可以有效地实现视网膜图像马赛克。

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