资源列表
image-watershedprogre
- 本程序通过水平集和分水岭分割算法对图像进行处理，非常不错的程序代码-This program segmentation algorithm and watershed level set by the image processing, a very good program code
test_contours
- 轮廓识别代码，需要你改变代码中加载照片的路径-Contour identification code, you need to change the path in the code to load photos
t4.tar
- 基于CGLX的小程序，可以作为学习CGLX的入门程序，它可以测试你的CGLX开发环境和部署环境，学习简单的CGLX编程方法-Based CGLX small program that can be used as learning CGLX entry program that can test your CGLX development environment and deployment environment, learn simple programming method CGLX
t2.tar
- 基于CGLX的一个小程序，可以用来测试你的CGLX开发环境和部署环境。-Based CGLX a small program that can be used to test your CGLX development environment and deployment environment.
t3.tar
- CGLX based codes including the project file.It can be build only with opengl.它时一个简单CGLX程序，可以通过pirconfig启动，也可以独立启动-CGLX based codes including the project file.It can be build only with opengl
t1.tar
- CGLX based codes including the project file.It can be build only with opengl
t0.tar
- CGLX(cross-platform graphic lib)开发的小程序，它完全可以通过open gl编译。-CGLX (cross-platform graphic lib) to develop a small program that can be compiled completely open gl.
PCAcodeandtheory_
- PCA原理以及实现代码，内容完整，可以复制到matlab软件中直接运行，只需改动图像路径即可。-PCA principle and implementation code, content integrity, and can be copied to directly run matlab software, just change the image path.
LDA
- 线性判别式分析(Linear Discriminant Analysis, LDA)，也叫做Fisher 线性判别(Fisher Linear Discriminant ,FLD)，是模式识别的经典算法,新手学习的良好素材-Linear discriminant analysis (Linear Discriminant Analysis, LDA), also known as Fisher linear discriminant (Fisher Linear Discriminant, FL
1388654382
- 驱动代码 GC6123 sp6530-6531平台 GC6123是一款8万像素的摄像头Cmos 芯片-** File Name: Sensor_GC6123.c * ** Author: Haydn_He * ** Date: 2012-11-16
VRML
- HTML与VRML的结合，HUD控制板面的设计-Combine HTML and VRML, HUD Control Panel Design
two_phase_sparse_ORL
- 两阶段测试样本稀疏表示方法具有识别率高的特点，有助于准确分类测试样本，但稀疏方法迭代时间长，效率较低，因此仍需要与其他方法进行结合-Two-stage test sample sparse representation method has high recognition rate, helps to accurately classify the test samples, but sparse iterative approach for a long time, less efficie