hough变换提取椭圆matlab源程序

说明：  classic goldstein filter by matlab for interferogram phase noise filtering(classic goldstein filter by matlab)

本程序将SIFT与边缘结合起来用来做图像配准 先用SIFT检测关键点 用CANNY算子进行边缘检测 进而优化选取最佳匹配点 进行向量匹配 图像配准。 (The program will combine the SIFT and the edge of image registration is used to make the first detection of the key points with SIFT operator with CANNY edge detection and then select the best match point optimized vector matching image registration.)

将含多个目标的图像减去背景图，再将所得图进行阈值化处理得二值图像，再对二值图像进行标记，然后跟含多个目标的图像进行处理，对这个图像进行多目标标记(Containing a plurality of target image by subtracting a background image, and the resulting graph threshold processing binary image to be marked, then the binary image, and then processed with an image containing a plurality of target, this image multi-target mark)

一个关于hough和radon变换的实例,实现了时钟图形做图.(and one in Chinese on hough transform examples and achieve a clock graphic graphics.)

利用灰度图 建共生矩阵 利用特征值 对纹理图像分割(The use of grayscale co-occurrence matrix to build the use of characteristic values of the texture map would like to partition)

说明：  用VC实现数字减影算法，通过模板匹配，再对两幅图像相减，即用盈片中减去蒙片，从而实现突出血管区域的目的。【源码加文档说明】(Using VC to achieve digital subtraction algorithm, template matching, and then the subtraction of two images, that the film with surplus minus mask, in order to achieve the purpose of highlighting vascular region. 【Source】 plus documented)

样条函数用于图像放大；可放大任意倍数；五次样条函数的构造 (five splines function)

经典的聚束SAR成像算法-极坐标格式成像算法(A typical imaging algorithm of spotlight mode SAR)

利用gabor滤波器方法提取视网膜血管，在提取之前进行预处理。gabor滤波器参数选择的是3个角度，12个方向(Gabor filter method is utilized to extract the retinal blood vessels, preprocessing before extraction. Gabor filter parameter selection are three angles, 12 direction )

Image segmentation systems have the potential to dramatically improve the performance of Digital image processing. Segmentation procedures partition an image into its constituent parts or objects. In general, segmentation is one of the most difficult tasks in digital image processing. Detection of salient image regions is useful for applications like image segmentation, adaptive compression, and region-based image retrieval. This problem can be tackled by mapping the pixels into various feature spaces, which are subjected to various grouping algorithms It present a novel method to determine salient regions in images using low-level features of luminance and color. The method is fast, easy to implement and generates high quality saliency maps of the same size and resolution as the input image.