Voigt Function Algorithm for High-Precision Computation in MATLAB

This forked from an existing project. Ellipse features are commonly encountered in machine vision, and high-precision detection of ellipse centers is often required. If you are interested in how to precisely draw ellipses, refer to my earlier share: High-quality Ellipse Detection. Illustration: This is the source code for the paper. Important: Please use the citation of our IEEE TIP version instead of the arXiv version. The main contribution of the proposed ellipse detector is to both accurately and efficiently detect ellipses in images, which is universally considered as a tough and long-standing problem in the ellipse detection field.

介绍如何使用 MATLAB 实现对图像的 打码 功能。主要步骤包括加载图像、选择要打码的区域，并应用 模糊 处理。最后，保存修改后的图像以供使用。

在 MATLAB 中，您可以调整变量的显示精度，以便更清楚地查看计算结果的小数位数。使用 format 命令可以设置不同的显示精度。例如，format long 可以显示更多的小数位，而 format short 则显示较少的小数位数。通过这种方式，您可以根据需要控制输出的精度，以适应不同的计算需求。 以下是一些常见的格式设置：- format short：显示 4 位小数。- format long：显示 15 位小数。- format bank：显示 2 位小数，适合货币表示。 调整显示精度不会改变变量的实际值，只影响输出显示。

Rootshufflem is a Matlab function designed for sorting the roots and eigenvalues of a matrix. This tool enhances the analysis of polynomial equations and dynamic systems by providing a systematic way to organize and manipulate eigenvalue data.

本项目建立PCA模型，使得PCA算子可以在任意时刻应用。实现基于MATLAB的PCA算法。

Function temp_matching(t1, t2, upl_1, lor_1, upl_2, lor_2, th, cal) Function Purpose This function performs image matching between two input images. The process utilizes upper and lower coordinates for both images to match corresponding features based on a threshold and calculation method. Input Parameters: t1, t2: Input image data. upl_1, lor_1: Upper and lower boundary coordinates of image 1. upl_2, lor_2: Upper and lower boundary coordinates of image 2. th: Threshold value for matching. cal: Calculation method for matching accuracy. Output Variables: mt1, mt2: Matched points in image 1 and image 2. upl_mt1, lor_mt1: Upper and lower boundary coordinates for matched points in image 1. upl_mt2, lor_mt2: Upper and lower boundary coordinates for matched points in image 2. match_weight: Matching confidence weight. err: Error term indicating matching accuracy. Key Concepts: Image matching is crucial in computer vision tasks for feature recognition, alignment, and more. This function helps achieve optimal alignment between two sets of data by considering boundary and threshold conditions.

用于小波神经网络MATLAB程序模拟，建议初学者好好看看，有一定作用。

在MATLAB中实现图像拼接算法的基本步骤包括：1. 读取输入图像；2. 提取特征点并匹配；3. 计算变换矩阵；4. 进行图像拼接；5. 调整拼接结果。

使用Dijkstra算法，寻求由起始点s到其他各点的最短路径树及其最短距离。