资源列表
radonbianhuan
- 霍夫变换是图像处理中识别几何形状的一种方法，在图像处理中有着广泛应用，霍夫变换不受图形旋转的影响，易于进行几何图形的快速变换。基于霍夫变换的改进方法也有很多，其中一个重要的方法是广义霍夫变换，可以用来检测任意形状的曲线。-Hough transform is a method of image processing to identify geometric shapes, with a wide range of applications in image processing, graphi
image
- 用vc++打开显示BMP图像 以及对图像进行浮雕处理-With vc++ open display BMP images and image processing relief
matlab
- 这是一个matlab基于主成分分析的特征提取的源代码-This is a matlab based on principal component analysis, feature extraction source code
fresnelt_2d
- 本程序模拟光学图像的菲涅尔变换过程，近似模拟光学图像信息在自由空间传播过程中的衍射现象-This procedure simulated Fresnel transform process of optical image, approximate optical image diffraction phenomenon in the process of information transmission in free space
EmgucvTest
- 用c#和emgucv实现了1.显示图像的直方图，2.直方图均衡化，3.中值滤波，4.sobel算子，roberts算子和Prewitt算子的边缘提取。各算法均自己实现不是调用函数。但是读图片的信息是用的emgucv。希望和同样刚接触emgucv的同学共同进步-With c# and emgucv achieve a 1 shows a histogram of the image, 2. Histogram equalization, 3 median filter, 4.sobel opera
FTVd
- 快速TV算法用于图像去模糊、图像去噪等，其中TV模式包括两种-TV went fast algorithm for image blur, image denoising, which includes two TV mode
firedetectImage
- 基于OpenCV开发的烟火识别代码,初学者或者有一定编程能力的人可以参考-OpenCV on the development of pyrotechnic identification code
LLE
- Locally linear embedding(LLE)是一个经典的流形学习降维方法，通过LLE发现高维数据潜在的内在维数，可以简化计算、方便可视化处理等。-Locally linear embedding (LLE) is a classic manifold learning dimensionality reduction method, discovered by LLE potential inherent high dimensional data dimension, the c
aptogo-FaceTracker-iPhone
- 人脸跟踪的iOS项目，里面包含了3.2版的opencv框架，可直接应用，一个很好的iOS+OpenCV的应用实例-Face tracking iOS project, which includes a version of the 3.2 opencv framework, application examples can be directly applied, a good iOS+ OpenCV s
LMNN
- 单核处理多特征融合(matlab实现——有数据提供）-Single-core processing multi-feature fusion
xiaobo
- 小波分次谐波分解 用来解决傅里叶分解难以处理的分数次谐波-Harmonic decomposition wavelet decomposition is used to solve difficult to deal with fractional Fourier harmonics
L1_classifier
- 基于1范数优化和二维随机映射的一种新的算法，实现图像识别，内容涉及到最先进的压缩感知采样-A new algorithm based on a two-dimensional random norm optimization and mapping, image recognition, which relates to the most advanced sampling compressed sensing