基于人工神经网络(ANN)和随机样本一致性(RANSAC)的稳健针识别

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI:10.1109/AIPR.2012.6528219

Jaewon Chang

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引用次数: 2

摘要

在本文中，我们提出了一种在立体图像中识别半圆型手术针的算法。利用人工神经网络(ANN)从两幅立体图像的针尖分割开始进行识别。然后，通过基于强度的匹配，将片段中的点与彼此的立体图像进行匹配，然后将其重新投影到三维空间中，并将其拟合到三维圆中。最后，使用随机样本共识(RANSAC)和已知的针头规格来估计针头的圆周。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Robust needle recognition using Artificial Neural Network (ANN) and Random Sample Consensus (RANSAC)

In this paper, we suggest an algorithm for a half-circle-like surgical needle recognition in stereo image. The recognition starts from segmentation of needle in both stereo images using Artificial Neural Network (ANN). Next, the points in the segments are being matched to each other stereo image through intensity based matching, and then re-projected to 3D space which will be fitted to 3D circle. Finally, estimate the circle of the needle using RANdom SAmple Consensus (RANSAC) and known specification of the needle.

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2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

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