细胞图像分割

厦门大学OJ上的ACM题1071目前最好算法,原创作品()

实现椭圆几何畸变矫正，将原始椭圆形变的图像，矫正为矩形正常图像(The correction of elliptical geometric distortion is achieved, and the original elliptical image is corrected to rectangle normal image.)

本文件是有关低通滤波器的程序，主要介绍了高斯低通滤波器。(This document is related to the low-pass filter procedure, introduces the Gaussian low-pass filter.)

图像纹理特征提取在图像处理中有着非常重要的作用，这个是提取图像纹理特征的程序！(Image texture feature extraction in image processing has a very important role, this is the image texture feature extraction procedure!)

处理图像，利用提出高频的方法，把图像中的裂纹提取出来。能比较好的把背景图像和裂纹分割开来。(Processing images, the use of the proposed high-frequency method of the cracks in the image extracted. Good background image and crack separated.)

几幅SAR图像。对SAR图像处理的童鞋很有用的。(Pieces of SAR images. Of SAR image processing is useful for students.)

孔径雷达图像固有的相干斑噪声严重降低了图像的可解译程度，影响了后续目标检测、分类和识别 等应用。因此，SAR图像的相干斑抑制问题一直是SAR图像应用的重要课题之一。一个理想的去斑算法应该在平 滑的同时保持图像的边缘等细节不受损失，目前存在各种各样的算法，但没有一种方法能够完美的满足这一要求。 本文对SAR图像的相干斑抑制问题进行了全面系统的研究，分析了相干斑的形成原因，总结了目前存在的相干斑 抑制算法的主要思路，介绍了具有代表性的算法，并对各种算法的性能进行了定性分析和比较，给出了去斑算法定 量评估方法(The inherent speckle noise of the aperture radar image seriously reduces the degree of interpretation of the image, and affects the detection, classification and recognition of the subsequent targets. And other applications. Therefore, the problem of speckle suppression in SAR images has been one of the most important topics in the application of SAR images. An ideal speckle algorithm should be flat At the same time, the details such as edges of images are not lost. There are various algorithms at present, but none of them can satisfy this requirement perfectly.)

在VS2012平台上利用C++语言和opencv实现对图像的分块，并保存分块图像(Using the C++ language and OpenCV to block the image on the VS2012 platform and save the block image)

详细设计了基于颜色直方图的图像检索 效果不错 计算速度快(Detailed design of the color histogram-based image retrieval good results fast calculation)

一个车牌定位方法，包括图像的灰度化，二值化，数学形态学的处理，滤波等(A license plate location methods, including gray-scale images, the binarization, mathematical morphology processing, filtering, etc.)