摘 要
本文针对曲面拟合及其在图像处理中的应用进行了研究。在绪论中，介绍了曲面拟合在图像处理中的作用，并针对传统曲面拟合算法和基于深度学习的曲面拟合算法进行了详细阐述。接着，在图像处理中的应用研究部分，包括了二维图像中基于曲面拟合的物体识别、三维图像中曲面重建及其应用、曲面拟合在图像配准和融合中的应用等方面的内容。通过实验设计和结果分析，比较了不同算法在曲面拟合的精度和效率上的差异，并分析了其优缺点。最后，总结了研究成果及贡献，并讨论了实验结果的局限和未来的研究方向。本文的研究成果是针对曲面拟合在图像处理中的应用展开的，具有一定的应用价值。实验结果表明，基于深度学习的曲面拟合算法具有更高的精度和效率，但同时对数据量和训练时间也提出了更高的要求。在实际应用中，需要根据具体需求选取不同的算法进行处理。本文的研究仍存在一些局限性需要进一步探索和完善，例如如何处理不稳定的曲面数据等问题。未来的研究方向是进一步探索不同应用场景下曲面拟合算法的优缺点，针对性地优化算法，并结合其他相关技术进行综合应用，提高图像处理的效率和精度。
关键词：曲面拟合；图像处理；深度学习
Absract
In this paper, surface fitting and its application in image processing are studied. In the introduction, the role of surface fitting in image processing is introduced, and the traditional surface fitting algorithm and the surface fitting algorithm based on deep learning are elaborated. Then, the application research in image processing includes ob ject recognition based on surface fitting in two-dimensional image, surface reconstruction and its application in three-dimensional image, and the application of surface fitting in image registration and fusion. Through experimental design and result analysis, the differences of accuracy and efficiency of different algorithms in surface fitting are compared, and their advantages and disadvantages are analyzed. Finally, the research achievements and contributions are summarized, and the limitations of experimental results and future research directions are discussed. The research results of this paper are aimed at the application of surface fitting in image processing, which has certain application value. The experimental results show that the surface fitting algorithm based on deep learning has higher accuracy and efficiency, but also puts forward higher requirements on the amount of data and training time. In practical application, different algorithms need to be selected for processing according to specific requirements. There are still some limitations in this study that need further exploration and improvement, such as how to deal with unstable surface data and so on. The future research direction is to further explore the advantages and disadvantages of surface fitting algorithm in different application scenarios, optimize the algorithm specifically, and combine other related technologies for comprehensive application, so as to improve the efficiency and accuracy of image processing.
Key words：Surface fitting; Image processing; Deep learning
目录
摘 要 I
Absract II
引言 1
1文献回顾 2
2.表面拟合方法的探讨 3
2.1基本的数学模式 3
2.2传统表面拟合方法 5
2.3利用深度神经网络进行表面拟合 5
3.表面拟合在图象处理中的应用 6
3.1利用表面拟合技术在2D图象中的目标识别 6
3.2表面重构技术在3D图象中的应用 7
3.3表面拟合技术用于图象的配准与融合 7
4.试验的设计和结果的分析 8
4.1数据的获取和加工 8
4.2试验装置和试验方法 9
4.3对试验数据的分析 9
结论 10
参考文献 12
