GTYLEA964

针对同一场景可见光与红外图像的融合问题,提出了一种新的多尺度对比度塔图像融合方法. 该方法 利用对比度金字塔数据结构得到图像的多分辨序列,采用基于视觉特性的融合算子在图像的相应各级上融 合源图像的细节,再通过金字塔逆变换重构出最终融合图像. 这种图像处理方法具自适应性,不随各自输入 图像的灰度特性而改变,同时增强了融合图像的对比度,产生了较好的视觉处理效果.(for the same scene of visible and infrared images of integration, proposed a new multi-scale tower contrast images. The use of contrast pyramid data structure Image multiresolution sequence, based on visual characteristics of the integration operator of the image corresponding levels of integration details of the source image, through the inverse pyramid transform eventually fused images. This image processing method has adaptability without their input image with the gray characteristics change, while enhancing the integration of the contrast of the image. have a better visual effect.)

图像融合里边剪切波变换方法的工具箱，欢迎下载(Shear wave transformation method for image fusion in the toolbox, welcome to download)

CZT变换进行信号频谱分析，解决了FFT法频域分辨不高的问题，源码开发，特别适用(Chirp Z Transform)

一个有关Hsv颜色空间的颜色量化程序，颜色量化并画出相关直方图(Hsv color space on a color quantization process)

基于Mean Shift的图像分割过程就是首先利用Mean Shift算法对图像中的像素进行聚类，即把收敛到同一点的起始点归为一类，然后把这一类的标号赋给这些起始点，同时把包含像素点太少的类去掉。然后，采用阈值化分割的方法对图像进行二值化处理 (Mean Shift Based on the process of image segmentation is the first to use the image Mean Shift algorithm for clustering of pixels, that is, to converge to the same point as a starting point to the category and then assigned to this type of labeling of these starting points, at the same time to contain too few pixels to remove the category. Then, using thresholding segmentation method of binary image processing)

adaboost的实现算法，可进行人脸的样本训练(implementation of the AdaBoost algorithm, can be a sample of people face training)

数字图像处理，边缘提取，分割，湖面面积计算matlab程序(Image processing, edge detection, segmentation, the lake area)

说明：  随机中点位移法matlab程序代码，生成三维山地模型(Random Midpoint Displacement Method for Mountains Generation)

对彩色图像进行小波融合，融合后图像在视觉上有了很大的改善。(Color images of wavelet fusion images visually a great improvement.)

实现了对10个数字音的识别，是学习PCA特征提取的很好的学习资料，包括调制，解调，信噪比计算。( To achieve the recognition of 10 digital sound, Is a good learning materials to learn PCA feature extraction, Includes the modulation, demodulation, signal to noise ratio calculation.)