color transfer

This project implements the technique of color transfer between images as described in the paper by Erik Reinhard, Michael Ashikhmin, Bruce Gooch, and Peter Shirley. One of the most common tasks in image processing is changing the colors of an image. This article presents a more general color correction method, which borrows color characteristics from one image to apply to another. The algorithm follows these steps: RGB to ℓαβ color space conversion: The transformation of the RGB color space into the ℓαβ color space, where the ℓ axis represents the lightness channel, and the α and β channels represent the color-opponent yellow-blue and red-green channels, respectively. Statistical and color correction: First, the mean value is subtracted from each channel's data points. Then, the data points are scaled by factors determined by the standard deviation of each respective channel. Final conversion to RGB: The result is converted back into the RGB color space. The MATLAB code for this procedure is contained in the color_transfer_built.m file. In lines 3 and 4 of the file, the source and target variables are modified to change the source and target images for color transfer.

在IT行业中，数据库管理和数据迁移是一项常见的任务。Access和Excel都是Microsoft Office套件中的重要工具。将详细介绍如何通过VB（Visual Basic）编程，利用ADODB来实现在Access和Excel之间传输数据。首先，引入ADODB相关的库： Imports Microsoft.Office.Interop.Excel Imports ADODB Dim conn As New Connection Dim rs As New Recordset conn.Open \"Provider=Microsoft.ACE.OLEDB.12.0;Data Source=C:pathtoyourdatabase.accdb;Persist Security Info=False;\" rs.Open \"SELECT * FROM Table1\", conn, adOpenStatic, adLockOptimistic Dim excelApp As New Excel.Application Dim workbook As Excel.Workbook = excelApp.Workbooks.Add() Dim worksheet As Excel.Worksheet = workbook.Sheets(1)

Simultaneous Heat Transfer Search (SHTS) is a single-objective optimization technique designed for unconstrained problems. Unlike traditional heat transfer search, which utilizes only one heat transfer mode per iteration, SHTS divides the population and simultaneously applies all three heat transfer modes. The main differences between SHTS and HTS can be found in the following references: Synchronous Heat Transfer Search for Costly Numerical Optimization, 2016 IEEE Congress on Evolutionary Computation (CEC), 2016, pp. 2982-2988; IEEE Paper Link Simultaneous Heat Transfer Search for Single-Objective Real Parameter Optimization Problems, TENCON 2016 IEEE, pp. 2138-2141, 2016; IEEE Paper Link Note: If N is the population size, exactly N function evaluations are required during a complete iteration of SHTS. For T iterations, the total number of function evaluations will be NT.

使用两种边界颜色和一种中心颜色创建颜色图。此方法在包含正值和负值时非常有用，中心颜色（通常为白色）代表零。用户可以定义边界和中心颜色，以及组成颜色图的段数。所有颜色输入需采用RGB三元组格式（例如[0 0 0]表示黑色）。生成颜色图后，使用：colormap(gca,newColormapName)上传到当前图形。请参阅函数文本（注释）以获取进一步的描述和示例用法。如果存在任何错误，请告诉我。

我自己写的图像色彩平衡代码，其中h(i)为r、g、b的平均值，lh为h(i)的平均值，h(i)/lh-1为平衡基，s(i)为加权系数。

MATLAB开发-三维彩色条形图。任意着色的三维条形图，通过简单的编程实现多种色彩效果，提升数据可视化的表现力和观赏性。

Traffic_sign_recognition: 使用定向梯度直方图（HOG）和基于色域的功能识别交通标志。采用支持向量机（SVM）对图像进行分类，提取交通标志的显著特征，提升识别准确性和可靠性。

To calculate the Signal-to-Noise Ratio (SNR) for a color image, simply open the image file in MATLAB and input the file path of the image. This process allows you to determine the SNR in decibels for the image based on its signal and noise characteristics.

边缘检测在图像处理中起着至关重要的作用。在本教程中，我们将展示如何使用OpenCV和MatLab在Lab色彩空间中实现边缘检测。具体步骤如下： 首先，将输入的RGB图像转换为Lab色彩空间。 在转换后的图像中，应用边缘检测算法，例如Canny边缘检测。 观察处理后的图像，分析边缘检测的效果。 通过此方法，Lab色彩空间的优势在于它更好地分离了色度和亮度信息，有助于提高边缘检测的准确性。 代码示例（OpenCV）： import cv2 import numpy as np # 读取图像 img = cv2.imread('image.jpg') # 转换为Lab色彩空间 lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab) # 提取亮度通道 l, a, b = cv2.split(lab) # 应用Canny边缘检测 edges = cv2.Canny(l, 100, 200) # 显示结果 cv2.imshow('Edges', edges) cv2.waitKey(0) cv2.destroyAllWindows() 此代码展示了如何使用OpenCV处理Lab色彩空间中的边缘检测。 MatLab代码示例： img = imread('image.jpg'); % 转换为Lab色彩空间 lab = rgb2lab(img); % 提取亮度通道 l = lab(:,:,1); % 应用Canny边缘检测 edges = edge(l, 'Canny'); % 显示结果 imshow(edges); 通过这些步骤，您可以在Lab色彩空间中准确地进行边缘检测，提升图像处理的质量。

用户可以在Munsell颜色图表中进行扫描，此代码将加载图像文件，以确定与每种Munsell颜色相关的La*b值。用户告诉代码每个图像上存在多少个色样，然后选择每个色样的中心点，并提供颜色名称。该代码对用户选择的点周围的框中的像素进行平均，可以在代码中调整框的大小。