Hyperspectral-image_RX-detection

图像识别起步。裂纹图像二值化及其骨架提取算法.(Image recognition.Crack image binarization and skeleton extraction algorithm is proposed.)

选择一副灰度图像，用区域分裂合并法对其进行分割(Choose a grayscale image, with its regional divisions split merge method)

说明：  《视觉SLAM十四讲》高翔，完整版，前10章。对视觉定位，机器学习，深度学习有很大帮助(Gao Xiang, the complete version of vision slam 14 lectures, is very helpful for vision positioning, machine learning and deep learning)

关于图像融合的评价标准的MATLAB源代码。(Image Fusion on the evaluation criteria of the MATLAB source code.)

说明：  采用考虑湍流内外尺度影响的退化模型来恢复大气湍流降质图像，运用包含了湍流内外尺度影响的波结构函数、折射率谱以及薄透镜成像的退化函数导出了新的退化模型。(A new degradation model includling the influence of outer-and inner-scale of turbulence is presented to restore atmospheric turbulence degradation images. )

数字图像处理中的几何变换，包括图像的平移，图像的镜像，图像转置，图像的缩放，图像的旋转以及三种插值算法(The geometric transformation in digital image processing includes image translation, image mirroring, image transposition, image scaling, image rotation, and three interpolation algorithms)

实现一个高精度指针仪表读数自动识别系统．用数码摄像头采集仪表的图像，然后利用数字图像处理技术识别刻度和指针，根据指针和刻度的相对位置计算指针读数值．使用条件霍夫变换（Constrained HOUGH Transfer）和中心投影分析法实现刻度的全自动识别，采用快速中心投影法进行指针检测与识别，识别速度达到68ms．实验结果表明该方法比传统方法速度快、精度高，系统读数平均误差仅为0．016％(To achieve a high precision automatic identification system pointer meter reading. Instrument with a digital camera capture images, then using digital image processing technology to identify scale and pointer, pointer and scale according to the relative position of the pointer readings calculated. Use Hough transform (Constrained HOUGH Transfer) and center projection analysis method to achieve scale automatic identification method using fast center projection target detection and recognition, recognition speed of 68ms. Experimental results show that faster than traditional methods, high accuracy, the system average error of only 0.016 of reading)

C# WPF 图片局部放大镜 示例源码

在做图像处理时用遇到图像的阈值分割，这是几种图像的阈值分割方法(In doing image processing with experience image threshold segmentation, which is one of several image thresholding segmentation method)

全参考型视频质量评价方法，基于机器学习和视频特征提取，发表与2012年TMM(FR VQA method based on machine learning and video feature )