ostufish

复杂场景中的红色交通标志牌检测与识别，结合颜色与圆形的标志牌定位，然后提取特征，最后用模板匹配的方法实现识别，内附图片和程序说明，对初学者有帮助(Red detection and recognition of traffic signs in complex scenes, the combination of colors and circular signs positioning, and feature extraction, the last with template matching methods to achieve recognition, included pictures and a description of the procedures for beginners)

这是一个利用导数光谱分析的方法检测特征吸收波段的源代码，可供从事高光谱图像处理的朋友分享。(This is a use of derivative spectroscopy method of the characteristic absorption band of the source code, available to engage in high-spectral image processing share.)

说明：  一种ENVI编程语言，用于ENVI二次开发便于处理高光谱图像(An envi programming language, used in the secondary development of envi, easy to process hyperspectral images)

VC图像编辑软件 就要卷积模糊和卷积模糊加噪 以不同方法（大于等于2）实现图像的模糊效果（局部或全局）。并以BMP格式存储。(soft)

图像分割 基于区域的分离和合并 实例 自己写的(SPLITMERGE Segment an image using a splil-and-merge algorithm.)

说明：  边缘检测是图像处理和计算机视觉中的基本问题，边缘检测的目的是标识数字图像中亮度变化明显的点。图像属性中的显著变化通常反映了属性的重要事件和变化。这些包括（i）深度上的不连续、（ii）表面方向不连续、（iii）物质属性变化和（iv）场景照明变化。边缘检测是图像处理和计算机视觉中，尤其是特征提取中的一个研究领域。 一阶:?Roberts Cross算子，Prewitt算子，Sobel算子， Kirsch算子，罗盘算子； 二阶： Marr-Hildreth，在梯度方向的二阶导数过零点，Canny算子，Laplacian算子。(Edge detection is a basic problem in image processing and computer vision. Significant changes in image attributes usually reflect significant events and changes in attributes. These include (I) discontinuities in depth, (ii) discontinuities in surface orientation, (iii) changes in material properties, and (iv) changes in scene lighting. Edge detection is a research field in image processing and computer vision, especially in feature extraction.)

该文档的主要功能就是利用vc编写一段代码实现摄像头采集图像。(The main function of the document is vc write a piece of code to achieve the camera capture images.)

对模糊图像进行复原（代码中样例）后，进行ISNR值的计算。由于复原后图像和原图像可能存在对齐问题，而该对齐问题不影响复原结果的主观效果，因此该代码首次考虑这一问题并给出相应的图像对齐及ISNR计算方法。(Restoration of blurred images (sample code), the calculation ISNR values. Due to possible alignment problems and the restored image after the original image, and the alignment problem does not affect the results of the subjective effects of recovery, so the code for the first time to consider this issue and the corresponding image alignment and ISNR calculation.)

ONSL0稀疏重构算法是NSL0的优化版 输入: y:测量值向量 A:测量矩阵 A_pinv:A的广义逆 输出: xr:重构信号 用于对信号或者图像的压缩重构(ONSL0 u7A00 u758F u91CD u6784 u7B97 u6CD5 u662FNSL0 u7684 u4F18 u5316 u7248 u8F93 u5165: y: u6D4B u91CF u503C u5411 u91CF A: u6D4B u91CF u77E9 u9635 A_pinv: A u7684 u5E7F u4E49 u9006 u8F93 u51FA: xr: u91CD u6784 u4FE1 u53F7 u7528 u4E8E u5BF9 u4FE1 u53F7 u6216 u8005 u56FE u50CF u7684 u538B u7F29 u91CD u6784)

说明：  文件说明： a1:将二进制文件转为Matalb数据 并批量保存为mat格式 a2:mat数据批量转为图片(RGB三通道） a3:RGB图片批量转灰度图、批量改变图片大小 a4:动图制作 a5:用训练好的网络对图像语义分割(Document description: A1: Convert binary files to Matalb data and save them in batch as mat format A2: mat data batch to picture (RGB three channel) A3:RGB Picture Batch Grayscale, Batch Change Picture Size A4: Motion Mapping A5: Segmentation of image semantics using trained networks)