MSRCR

基于马尔可夫随机场(MRF)、条件迭代算法ICM的图像分割源码(The MRF, the ICM-based image segmentation source code to source image segmentation based on the MRF ICM)

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.

用matlab的分水岭分割法实现图像分割，基于形态学，提取边缘(Using Matlab watershed segmentation method to achieve image segmentation)

说明：  sift算法实现的图像匹配程序，使用matlab编写的，多用于图像拼接，融合等领域。(The image matching program implemented by SIFT algorithm is written by MATLAB, and it is mostly used in image splicing, fusion and other fields.)

先对图像进行DCT变化再进行水印嵌入，用于图像加密，保护版权，在版权保护方面有较大的用途。(Digital Watermarking DCTembed)

说明：  织物疵点检测，有GUI界面，可自动识别疵点并分类(Fabric defect detection, GUI interface, automatic defect identification and classification)

说明：  Make_chaos 将图像进行置乱，生成乱序图，以便进行水印嵌入(Make_chaos will Scrambling images, generate disorder plans for embedding)

说明：  结合模糊C均值聚类算法与水平集方法的图像分割代码，分割效果良好。(The image segmentation code based on fuzzy c-means clustering algorithm and level set method has a good segmentation effect.)

：植物种类识别方法主要是根据叶片低维特征进行自动化鉴定。然而，低维特征不能全面描述叶片信息，识别准确率低，本文提 出一种基于多特征降维的植物叶片识别方法。首先通过数字图像处理技术对植物叶片彩色样本图像进行预处理，获得去除颜色、虫洞、 叶柄和背景的叶片二值图像、灰度图像和纹理图像。然后对二值图像提取几何特征和结构特征，对灰度图像提取 Hu不变矩特征、灰 度共生矩阵特征、局部二值模式特征和 Gabor 特征，对纹理图像提取分形维数，共得到 2183 维特征参数。再采用主成分分析与线性 评判分析相结合的方法对叶片多特征进行特征降维，将叶片高维特征数据降到低维空间。使用降维后的训练样本特征数据对支持向量 机分类器进行训练(plant species identification method is mainly based on blade automatic identification of low dimensional characteristics.However, can not fully describe blade low-dimensional feature information, identification accuracy is low, in this paper A kind of plant leaves recognition method based on multiple feature dimension reduction.First by digital image processing technology to the plant leaf color sample image preprocessing, obtain background color removal, wormhole, petioles, and the blades of a binary image, gray image and texture image.Then the binary image to extract the geometric characteristics and characteristics of structure and characteristics of gray image extraction Hu moment invariants, gray co-occurrence matrix feature, local binary pattern features and Gabor, to extract the fractal dimension of texture image, get 2183 d characteristic parameters.By principal component analysis and linear uation analysis method of combining the characteristics of blade more feature dimensi)

自适应算术编码的例子，使用的是整数算术编码(Examples of adaptive arithmetic coding, the use of integer arithmetic coding)