TV图像处理

CNN中的手写识别LeNet5实现，用python，进行了中文注释(CNN handwritten recognition LeNet5 implementation, using python, carried out in Chinese Notes)

把图像从RGB格式转换到YUV格式的一个函数，直接调用，管他有没有，上传了再说(Image conversion from RGB format to YUV format of a function, a direct call, though he did not upload to say)

3d 重建 包括特征点的提取，匹配，以及三维点云的建立(3d reconstruction ,including feature detection and 3 D points cloud ,the change from 2D to 3D)

对比度拉伸，对原始图像进行对比度拉伸，从而得到拉伸后的图像(tensile contrast, the original image contrast stretching, and thus drawing the images)

将原始载体图片经过一维冗余离散小波变换分成四个子频带，分别对每个子频带进行奇异值分解， 将水印图片灰度值直接按比例缩小嵌入到奇异值矩阵中，再将此矩阵进行一次奇异值分解得到奇异值当做此子带的奇异值， 重建此子带矩阵，而后将进行反冗余离散小波变换得到嵌入载体后的水印图像。(The original image after the one-dimensional vector redundant discrete wavelet transform is divided into four subbands, respectively, of each sub-band for the singular value decomposition, the watermark image gray value scale down directly embedded into the matrix singular value, then this matrix is ​ ​ a Singular value decomposition singular value as the singular values ​ ​ of this sub-band, the reconstruction of this sub-band matrix and after anti-redundant discrete wavelet transform watermark image after embedding the carrier.)

ITK的脑室图像分割,可以对脑部的图像用itk进行分割，用VTK来进行显示。(the segration of brain of ITK )

机器视觉之模糊图像复原，实现inverse Filter, Wiener Filter, Lucy-richardson 算法，里面包括PPT，代码和报告。非常有价值，值得参考！ (Machine vision blurred image restoration, achieve The inverse Filter, Wiener Filter, Lucy-richardson algorithm, which includes the PPT, the code and report. Very valuable and worth considering!)

综合多个特征的图像检索，包括颜色、形状和纹理等(Integrated multiple features for image retrieval, including color, shape and texture)

vc6.0 控制摄像头源代码，采用directxshow 效率很高，小弟参考了很多的资料才搞明白，和大家共享，希望对大家有帮助(camera control vc6.0 source code, the use of highly efficient directxshow,小弟reference a lot of information in order to engage in to understand, and share, in the hope that we have to help)

过特定人语音情感数据库的建立；语音情感特征提取；语音情感分类器的设计，完成了一个特定人语音情感识别的初步系统。对于单个特定人，可以识别平静、悲伤、愤怒、惊讶、高兴5种情感，除愤怒和高兴之间混淆程度相对较大之外，各类之间区分特性良好，平均分类正确率为93.7 。对于三个特定人组成的特定人群，可以识别平静、愤怒、悲伤3种情感，各类之间区分特性良好，平均分类正确率为94.4 。其中分类器采用混合高斯分布模型。(The system of speech emotion recognition)