图像拼接---去除黑边

能够将图像坐标转化为世界坐标，根据图像像素修改参数(Can transform image coordinates into world coordinates, and modify parameters according to image pixels.)

基于基元共生矩阵的纹理特征提取。通过5个基本的方格来检测图像并生成图像的基元共生矩阵，并从矩阵中提取其纹理特征信息。(Texton occurrence matrix based texture feature extraction)

近两年关于图像检索的重要参考文献，包括IEEE和国内CNKI上发表的顶级期刊(Image Retrieval over the past two years on the importance of references, including the IEEE and domestic CNKI top journals published)

将彩色图像灰度化，使在处理过程中数据处理简单化(gray color image, so that in the course of handling the data processing simplistic)

经典数字图像处理算法仿真，包括图像的傅里叶滤波及压缩，图像的DCT高通、低通滤波，图像直方图均衡化，图像平滑与锐化，图像的模糊化，哈夫曼编码等，以及GUI图形化界面。(Classic digital image processing algorithm simulation, including Fourier filtering and image compression, image DCT high-pass, low-pass filtering, image histogram equalization, image smoothing and sharpening, blurring the image, Huffman coding, as well as GUI graphical interface.)

use js and css show image change effect

说明：  最大熵双阈值分割算法，基于遗传算法优化的快速最大熵双阈值分割算法。(Maximum Entropy Double Threshold Segmentation and Fast Maximum Entropy Double Threshold Segmentation Based on Genetic Algorithms.)

火灾探测中火焰图像分割方法研究,比较实用。(Fire detection fire image segmentation, more practical.)

harris角点检测，并且实现两幅图像特征点的匹配，最终完成图像的拼接(harris corner detection, and realize two image feature point matching, the final completion of image stitching)

斯坦福大学的姚邦鹏开发的一个图像分类算法，使用random forest实现了图像的精细区域描述，赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试，程序完全可运行，且提供了MAC和Windows下的两种程序。(Image Classification: An Integration of Randomization and Discrimination in A Dense Feature Representation The goal of our method is to identify the discriminative fine-grained image region that distinguishes different classes. To achieve this goal we sample image regions from dense sampling space and use a random forest algorithm with discriminative classifier. Each node of the tree of random forest is trained and tested with fine-grained image patches combining the information from upstream nodes together. We implemented each node of the tree with a discriminative SVM classifier, which makes the node as a strong classifier. )