snake_examples

此程序是基于灰度共生矩阵的图像纹理特征提取算法，它进行了四个方向上的灰度共生矩阵的计算，从而得出他们的纹理特征向量，再求均值，使计算进度大幅度提高(This program is based on the GLCM texture feature extraction algorithm, which was four directions GLCM calculation to arrive at their texture feature vector, and then seek means to significantly increase computing progress)

结合SAR图像降斑以及频域增强的SAR图像增强技术(a SAR image enhancement method combined with despeckling and enhancement in the frequency domain)

VisualC++数字图像处理技术详解第2版光盘，第十二章(VisualC++ digital image processing technology Detailed Version 2 discs, Chapter XII)

单相光伏发电并网系统在孤岛运行时的simulink仿真(Islanding operation simulation)

说明：  运行jdxingtaixue文件，该程序用固定结构元素形态学对图像进行边缘检测(Run jdxingtaixue file, the program with a fixed structure element morphology of the edge detection)

说明：  分析音频，分割进行傅里叶变换，得出声谱图(Analyze the audio and get the spectrogram)

说明：  指定图像位置和大小的灰度共生矩阵的提取,程序自己编得,蛮好用的(Extraction of gray level co-occurrence matrix of specified image position and size)

说明：  一个用matlab编写的源程序，可以实现基于颜色直方图的图像检索(A source code written with matlab, you can achieve the color histogram-based Image Retrieval)

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.

Object Detection Combining Recognition and Segmentation, Eighth Asian Conference on Computer Vision (ACCV), 2007 源代码(Object Detection Combining Recognition and Segmentation, Eighth Asian Conference on Computer Vision (ACCV), 2007 source code)