搜索资源列表
NaturalLanguageSearch
- 自然语言搜索引擎，搜索结果显示在浏览器的右边。初级编程者下。-The user clicks 揝ay It To The Internet?and their sentences is processed by both a search engine and a popular AI chatbot like Hexbot. The search engine image results are displayed on the right and the chatbot抯 response
VBehchen
- 本程序适合于初学VB学员，此程序只是简单地对两张图像的合并！-This procedure is suitable for students beginners VB, this program simply on the two images merge!
MergeImage
- 合并两张不一样的图片，处理的效果还是挺好的-merge two images
pimed
- merge two images seamlessly.
face-merge
- 对两张图片合并，看起来合并效果不错，可以-Of the two images merge, the combined effect looks good, you can try
zfdg
- 图像中角点(特征点)提取与匹配算法,对于两幅相似的图像，通过角点检测算法，进而找出这两幅图像的共同点，从而可以把这两幅图像合并成一幅图像。(含程序)-Image corners (feature points) extraction and matching algorithms for two similar images by corner detection algorithm, and then find the two images in common, which can merge
ImgMatch
- 合并两幅图像为一副图像。ImgMatch.m 为主文件。采用的是兴趣点合并。-Merge the two images as an image based on interest point. ImgMatch.m is the main file.
111111111
- 图像合并，材料：两张图像，效果：两张图像合并在一张并显示出来。-Image merge, Material: two images, the effect is: two images merged In the one and displayed.
fuze1_main
- Two images can be fuse or merge on the basis of contrast using matlab program. this program fuses two images as well. Here in this zip folder two images with fuze program is atteching.
imageacc
- 调用opencv函数，读入两幅图像，并且将这两幅图像融合成一幅-Call opencv function that reads two images, and these two images merge into one
Mosaic
- Image mosaic based on sift sift means scale invarient feature transform we need to merge two images based on this method we can-Image mosaic based on sift sift means scale invarient feature transform we need to merge two images based on this method w
ImageMerge
- VC++ 合并两个或多个图片成一个图片，学数字图像处理基础-Vc++ to merge two or more images into an image, fundamentals of digital image processing
opencv-mix-pic-
- 将两张图片融合成一张，需要配置opencv的支持-The two images merge into one, you need to configure opencv support
vedio
- 目标检测与识别 1. 颜色检测 采集大量敌方机器人的图片数据，并进行训练，得到对方机器人的颜色区间， 并以此为阈值对整幅图像进行颜色检测，找到疑似敌方机器人的区域，量化 成二值图。 2. 滤除噪声点 对得到的二值图像进行开运算处理，滤除颜色检测结果中的噪声点。 3. 连通区域检测 对图像中的疑似区域进行连通区域检测，计算出每个疑似区域的外部轮廓， 用矩形近似表示。 4. 连通区域合并 根据连通区域之间的距离和颜色相似性，将距离接近且相似性高的连通区域