LBP1

展示了汽车行业的应用案例。任务是利用标定的可变形匹配来定位不同方向和位置的车门(3D variable model matching)

说明：  数字图像处理中非常有用的一些图片包括LENA等.(Digital image processing is very useful in a number of pictures, including LENA, BARBARA such.)

K均值算法实现图像的分割，分割效果可以达到预期的效果(K-means algorithm to achieve image segmentation, segmentation results can be achieved the desired results)

这是我本科的毕业设计做的关于医学图像的基于形状的图像检索。预处理用小波去噪 ，特征选用不变矩。(This is my undergraduate graduation design done on the medical images based on the shape of the image retrieval. Pretreatment with wavelet denoising, feature selection invariant moment.)

下面给出了48项泽尼克多项式，外加一项常数项。需要注意的是，读者并不需要严格按照下文所示的顺序排列这些泽尼克项，实际上在不同的应用和机构会采用不同的排列顺序。 表中的#0项是个常数或者说是平移项（piston term），这一项的系数也代表了平均光程差；而#1和#2项分别是x和y方向的倾斜项（tilt terms），#3代表了聚焦，因此，#1到#3项代表了波前的高斯或者近轴特性；#4和#5项代表了像散和离焦，#6和#7项代表彗差和倾斜，而#8项代表了3级像差和离焦，也就是说#4到#8项为3级相差项；同样地，#9到#15项代表了5级像差，而#16到#24项代表了7级像差，#25到#35项代表了9级像差，#36到#48项代表了11级像差。(Here are 48 Zernike polynomials, plus a constant term. It is important to note that the reader does not need to strictly arrange the Zernike entries in the order shown below, but in fact different orders of arrangement are applied to different applications and institutions. Table #0 is a constant or a shift (piston term), a coefficient of this term also represents the average optical path difference; while #1 and #2 are inclined X and Y direction (tilt terms), #3 represents the focus, therefore, #1 to #3 on behalf of the wavefront. Gauss or paraxial properties; #4 and #5 represent the astigmatism and defocus, #6 and #7 represent coma and tilt, and #8 represents the 3 order aberration and defocus, that is to say #4 to #8 is 3 level difference; similarly, #9 to #15 on behalf of the 5 order aberration #16 and #24, to represent the 7 level #25 to #35 aberration, on behalf of the 9 #36 to #48 aberration, on behalf of the 11 order aberration.)

用于分别从四个方向提取图形图像的纹理特征(For texture feature graphic images were extracted four directions)

MSER算法的实现，功能齐全，思路清晰！值得研究图像处理的人员参考！(MSER algorithm implemented, the function is all ready, clearthinking! )

基于曲波变换，小波变换的图像融合，图像检测的相关文献，有期刊，也有硕士论文，中文英文都有，都是近几年的文章，供大家参考学习(Based on curvelet transform, wavelet transform image fusion, image detection literature, there are journals, but also master' s thesis, Chinese, English has, in recent years are the article, for your reference learning)

模糊聚类的图像分割 C-均值聚类 FCM(Fuzzy clustering image segmentation )

opencv,图像处理，关于物体粘连问题的检测，基于凹性分析的分割方法(opencv, image processing, object sticking problem on detection, segmentation method based on analysis of the recess)