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Fully automatic modeling of image-objects out of example images

从示例图像中全自动建模图像对象

基本信息

批准号：
17500061
负责人：
WATANABE Toshinori
金额：
$ 1.41万
依托单位：
The University of Electro-Communications
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2006
项目状态：
已结题

项目摘要

The possibility of an automatic image-object modeling is studied and affirmative results are attained as follows.1.Object model extraction out of still imagesSuppose we can compress the original image largely by giving a new name to a set of clustered color regions, we may considerthe region set as an object. Using a few object plausibility measures side by side with this principle, we could succeed in extracting, fully automatically, structural descriptions of cartoons, faces, and playthings out of color images. The structural human model could also be extracted out of a still image made up of a few frames of a human walking video.2.Object model extraction out of a videoSe developed an algorithm composed of, the moving object extraction by background elimination, the border curve feature vector extraction, and the novelty analysis by voting from learned vectors to the incoming vector. Novel one is stored as a new model, otherwise only a model label (= recognition result) is output. Primitive human actions, i. e., walking and nodding, etc., could be extracted and used for further recognition in online real-time mode.3.Additional outcome of 1.Finding a set of clustered color regions in a segmented image is one of the most important tasks in 1 above. We reduced this problem into a graph matching problem, i. e., a maximum clique problem and proposed two efficient algorithms to solve it, both exploiting graph attribute information. One uses them in the process of maximum clique search and the other uses them for original graph reduction. Both are effective, but the latter dominates the former.4.Additional outcome of 2.The system in 2 above requires a melted video streams. So we need the decompression of a compressed video data. We examined a new possibility of video analysis directly in a compressed data domain and succeeded to track a walking person in MPEG compressed video.

1.静止图像中目标模型的提取假设我们可以通过给一组聚类的颜色区域赋予新的名称来大大压缩原始图像，我们可以将该区域集合重新命名为目标。使用几个对象的可扩展性措施与此原则并行，我们可以成功地提取，全自动，结构描述的卡通，面孔，和玩具的彩色图像。结构人体模型也可以从由几帧人体行走视频组成的静止图像中提取出来。2.视频中的对象模型提取我们开发了一种由背景消除的运动对象提取、边界曲线特征向量提取和从学习向量到输入向量的投票新奇分析组成的算法。新的模型被存储为新的模型，否则仅输出模型标签（=识别结果）。原始的人类行为，我。例如，走路和点头等等，3. 1的额外结果。在分割的图像中找到一组聚类的颜色区域是上面1中最重要的任务之一。我们把这个问题归结为一个图匹配问题，即图匹配问题。例如，最大团问题，并提出了两个有效的算法来解决这个问题，都利用图的属性信息。一个使用它们在最大团搜索的过程中，另一个使用它们的原始图约简。两者都是有效的，但后者占主导地位的前。4.额外的结果2。在2上述系统需要一个融合的视频流。所以我们需要对压缩后的视频数据进行解压缩。我们研究了一种新的可能性，视频分析直接在压缩数据域，并成功地跟踪一个行走的人在MPEG压缩视频。